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ObjectiveNegative Poisson’s ratio materials are widely used in various fields because of their unique properties of expansion in tension and contraction in compression.MethodsA honeycomb material with negative Poisson’s ratio was obtained by equal biaxial compression of the two-dimensional Voronoi model, and the effects of different compression ratios on its static tensile/compressive mechanical properties were studied by numerical simulation.ResultsThe results show that the tensile and compressive stress-strain curves of the two-dimensional negative Poisson’s ratio honeycomb prepared by the above method have typical "three-stage" deformation characteristics of porous materials, and show significant tensile and compressive strength asymmetry. Under uniaxial tension, the material shows obvious negative Poisson’s ratio phenomenon. With the increase of compression ratio, the elastic modulus and elastic Poisson’s ratio gradually decrease, and the yield strength and platform stress gradually increase. With the gradual increase of tensile strain, the plastic Poisson’s ratio of the material changes from negative to zero and then to positive. However, due to the mutual contact between the inner edges of the material, the inward deformation of the material is limited. Therefore, the material exhibits positive Poisson’s ratio behavior under uniaxial compression, and its elastic modulus, yield strength, elastic/plastic Poisson’s ratio and platform stress increase with the increase of compression ratio. In addition, the negative Poisson’s ratio material is anisotropic in tension and isotropic in compression.

Self-similar multi-cell structures have been widely applied in engineering fields for their excellent mechanical properties and multi-functional characteristics. In this paper, self-similar inspired multi-cell tubes are constructed by replacing the substructures according to different strategies. The hybrid tubes are built by the space-filling and tessellation of these self-similar inspired multi-cell tubes. The effect of the diameter, layout and hierarchical order are experimentally investigated. The tessellation and space-filling of these multi-cell tubes are numerically and theoretically investigated. The results show that the specific energy absorption (SEA) increases with the hierarchical fractal order. The specific energy absorption of the 2nd order self-similar inspired multi-cell tubes increased by 342.5% and 116.5% compared to the 0th and 1st order multi-cell tubes. It is also found that the increase of the side length is not an effective method to improve the crashworthiness of the lower-order multi-cell tubes. Finally, a theoretical model of the angle element was constructed to predict the mean crushing force of these multi-cell tubes. In general, our research provides an effective method for the design of self-similar inspired multi-cell structures with excellent crashworthiness.

High-quality ontologies are critical to ontology-based applications, such as natural language understanding and information extraction, but logical conflicts naturally occur in the lifecycle of ontology development. To deal with such conflicts, conflict detection and ontology repair become two critical tasks, and we focus on repairing ontologies. Most existing approaches for ontology repair rely on the syntax of axioms or logical consequences but ignore the semantics of axioms. In this paper, we propose an embedding-based approach by considering sentence embeddings of axioms, which translates axioms into semantic vectors and provides facilities to compute semantic similarities among axioms. A threshold-based algorithm and a signature-based algorithm are designed to repair ontologies with the help of detected conflicts and axiom embeddings. In the experiments, our proposed algorithms are compared with existing ones over 20 real-life incoherent ontologies. The threshold-based algorithm with different distance metrics is further evaluated with 10 distinct thresholds and 3 pre-trained models. The experimental results show that the embedding-based algorithms could achieve promising performances.

Schematic knowledge, an important component of knowledge graphs (KGs), defines a rich set of logical axioms based on concepts and relations to support knowledge integration, reasoning, and heterogeneity elimination over KGs. Although several KGs consist of lots of factual knowledge, their schematic knowledge (e.g., subclassOf axioms, disjointWith axioms) is far from complete. Currently, existing KG embedding methods for completing schematic knowledge still suffer from two limitations. Firstly, existing embedding methods designed to encode factual knowledge pay little attention to the completion of schematic knowledge (e.g., axioms). Secondly, several methods try to preserve logical properties of relations for completing schematic knowledge, but they cannot simultaneously preserve the transitivity (e.g., subclassOf) and symmetry (e.g., disjointWith) of axioms well. To solve these issues, we propose a cosine-based embedding method named CosE tailored for completing lightweight schematic knowledge in DL-Litecore. Precisely, the concepts in axioms will be encoded into two semantic spaces defined in CosE. One is called angle-based semantic space, which is employed to preserve the transitivity or symmetry of relations in axioms. The other one is defined as translation-based semantic space that is used to measure the confidence of each axiom. We design two types of score functions for these two semantic spaces, so as to sufficiently learn the vector representations of concepts. Moreover, we propose a novel negative sampling strategy based on the mutual exclusion between subclassOf and disjointWith. In this way, concepts can obtain better vector representations for schematic knowledge completion. We implement our method and verify it on four standard datasets generated by real ontologies. Experiments show that CosE can obtain better results than existing models and keep the logical properties of relations for transitivity and symmetry simultaneously.

When resolving logical contradictions in ontologies, Reiter's hitting set tree algorithm is often applied to satisfy the minimal change principle. To improve the efficiency, the researchers have proposed various algorithms by using a scoring function, defining new semantics or applying some heuristic strategies. However, these algorithms either sacrifice minimal change or are designed for less expressive ontologies like DL-Lite. In this paper, we propose a mathematic approach based on integer linear programming, which is an optimization problem of maximizing or minimizing a linear objective function, to deal with DL ontologies. Specifically, we define the integer linear programming-based model to resolve logical contradictions. To realize the model, we propose one algorithm to find a cardinality-minimal solution and two algorithms dealing with weighted ontologies. Our experiments are conducted over 70 real-life and artificial ontologies to compare our algorithms with those hitting set tree-based ones. Through the experiments, the prominent efficiency and effectiveness have been exhibited by our algorithms. They usually take about 0.4 s to find a solution while others spend more than 100 s in many cases. The experimental results also show that the first two algorithms could find the cardinality-minimal solutions and those with a minimal sum of weights, respectively.

Considering the residual stress, residual strain and residual strain energy, the influence of shot peening on the J-integral parameters of semi-elliptic surface crack front was quantitatively calculated and analyzed. The shot peening process was simulated, the fatigue crack was generated by changing the constraint condition and far field load was applied to calculate the J-integral and crack propagation rate. The effect of semi-elliptic surface cracks with different depth and different shot velocities on fracture parameters was analyzed. The results show that when shot velocity is a certain value, the reduction of the J-integral value after shot peening decreases with the increase of the crack depth compared with that without shot peening, which indicates that shot peening is more beneficial to suppress the fatigue crack propagation. When the crack depth is 0.3mm, the J-integral value of the deepest point of the crack front reduces from 4.25N/mm to 2.99N/mm, and the reduction is about 30.1%.The J-integral value decreases with the increase of shot velocity when crack depth is a certain value,therefore,increasing shot velocity is more beneficial to retard the fatigue crack propagation.

Compared to the traditional electrochemical power source, lithium ion batteries (LIBs) have the advantages of higher energy density, longer life, and absence of any memory effect, and thus have attracted widespread research interest around the world. After Sony Inc. invented and produced the first commercial 18650 cell, many domestic and international research centers and companies have promoted the industrialization of LIBs. With the development of LIB technology, its application scope has extended from traditional consumer electronics to the new energy vehicles (NEVs) and energy storage fields. NEVs include pure electric vehicles (PEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). LIBs have been the main driving force for PEVs to date, and their cathode technology development process has had three generations, i.e., the first using LiCoO2, the second using LiMn2O4 and LiFePO4, and the third generation using Li(NixCoyMn1-x-y)O2. With the development of cathode and anode materials with higher capacities and the increased reliability of LIB safety technology (including separators with higher temperature resistance, electrolytes with higher voltage resistance, and other protection methods), cells with higher energy densities and longer lives can be developed and applied in the future. These improvements will enable PEVs to travel longer distances, which is the most critical issue to customers. This paper provides a review of the development status of the power battery industry and an analysis of the direction of LIB technology with respect to the following: (1) the cathode/anode materials used, including the higher Ni content in Li(NixCoyMn1-x-y)O2, along with its structural modification, and the stability of silicon and improvements in its efficiency and cycle life; (2) the design technology, including the electrode and structure designs developed using simulation technology, theoretical modeling, and experimental methods based on Taguchi design; and (3) advances in process technology, including mixing and coating processes. Based on the above information, a clear picture of the technical direction was provided for LIBs in the PEV field.

The interaction integral method（IIM） is used to calculate stress intensity factors（SIF） of finite thickness semi-elliptical surface cracks. Results are compared with that predicted by the empirical equation and equivalent domain integral（EDI） method. The comparison with EDI method verifies computational accuracy of IIM for calculating 3 D crack problem. The SIF distribution along the semi-elliptical crack of different crack shape ratio and different finite thickness ratio conditions are calculated with the IIM and empirical equation. Relative error of empirical equation is evaluated. The results show that empirical equation produces larger error when the finite thickness ratio is large. Based on the analysis, the applicable range of the empirical equation is presented.

In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and "to develop, validate, improve, and implement serological testing and associated technologies" (https://www.cancer.gov/research/key-initiatives/covid-19/coronavirus-research-initiatives/serological-sciences-network). SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology standard reference material and first WHO international standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. SeroNet institutions reported development of a total of 27 enzyme-linked immunosorbent assay (ELISA) methods, 13 multiplex assays, and 9 neutralization assays and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. In conclusion, SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons. IMPORTANCE SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data.

Abstract Background Cancer serum protein profiling by mass spectrometry has uncovered mass profiles that are potentially diagnostic for several common types of cancer. However, direct mass spectrometric profiling has a limited dynamic range and difficulties in providing the identification of the distinctive proteins. We hypothesized that distinctive profiles may result from the differential expression of relatively abundant serum proteins associated with the host response. Methods Eighty-four antibodies, targeting a wide range of serum proteins, were spotted onto nitrocellulose-coated microscope slides. The abundances of the corresponding proteins were measured in 80 serum samples, from 24 newly diagnosed subjects with lung cancer, 24 healthy controls, and 32 subjects with chronic obstructive pulmonary disease (COPD). Two-color rolling-circle amplification was used to measure protein abundance. Results Seven of the 84 antibodies gave a significant difference (p < 0.01) for the lung cancer patients as compared to healthy controls, as well as compared to COPD patients. Proteins that exhibited higher abundances in the lung cancer samples relative to the control samples included C-reactive protein (CRP; a 13.3 fold increase), serum amyloid A (SAA; a 2.0 fold increase), mucin 1 and α-1-antitrypsin (1.4 fold increases). The increased expression levels of CRP and SAA were validated by Western blot analysis. Leave-one-out cross-validation was used to construct Diagonal Linear Discriminant Analysis (DLDA) classifiers. At a cutoff where all 56 of the non-tumor samples were correctly classified, 15/24 lung tumor patient sera were correctly classified. Conclusion Our results suggest that a distinctive serum protein profile involving abundant proteins may be observed in lung cancer patients relative to healthy subjects or patients with chronic disease and may have utility as part of strategies for detecting lung cancer.