Folded Sandwich Protective Structures against Blast and Impact Loads

In this thesis, novel folded truncated pyramid structures and a bi-directional load-self-cancelling square dome structure are proposed as the core of light-weight protective sandwich structures to resist blast and impact loads. Analytical derivations, numerical simulations, quasi-static and dynamic crushing tests are carried out to examine the dynamic crushing behaviours and energy absorption capacities of various designs for developing the best-performing core structures for blast and impact load resistance

SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks

Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and real-world signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech

Figures of speech, such as metaphor and irony, are ubiquitous in literature works and colloquial conversations. This poses great challenge for natural language understanding since figures of speech usually deviate from their ostensible meanings to express deeper semantic implications. Previous research lays emphasis on the literary aspect of figures and seldom provide a comprehensive exploration from a view of computational linguistics. In this paper, we first propose the concept of figurative unit, which is the carrier of a figure. Then we select 12 types of figures commonly used in Chinese, and build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe). Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type. On ConFiguRe, three tasks, i.e., figure extraction, figure type classification and figure recognition, are designed and the state-of-the-art techniques are utilized to implement the benchmarks. We conduct thorough experiments and show that all three tasks are challenging for existing models, thus requiring further research. Our dataset and code are publicly available at https://github.com/pku-tangent/ConFiguRe.Comment: Accepted to Coling 202

Polydopamine/graphene oxide coatings loaded with tetracycline and green Ag nanoparticles for effective prevention of biofilms

Bacterial adhesion and biofilm formation are significant challenges for medical devices and implants. Surface modification to alter the surface properties of biomedical device surfaces to prevent the biofilm formation is an important driving force for the development of anti-biofilm coatings. Here, a simple and feasible method to fabricate antibacterial coatings that combines the adhesion properties of polydopamine (PDA) and the high drug loading capacity of graphene oxide (GO). Tetracycline and green-synthesized silver nanoparticles were successfully assembled onto the coating surface, endowing the coating an anti-biofilm effect and exhibit strong inhibitory effect on S. aureus and E. coli biofilms by a factor of more than 1000 (3 log10 units). Kirby-Bauer diffusion test, colony forming unit (CFU) counts, biofilm topography studies and live/dead staining were used to evaluate the antibacterial activity of the coatings. This study is proposed that PDA/GO coatings loaded with antibiotics or silver nanoparticles can be used as a potential approach to prevent infection associated with implantable biomedical devices

Effect of the heating rate on the thermal explosion behavior and oxidation resistance of 3D-structure porous NiAl intermetallic

Porous NiAl intermetallic compounds demonstrate great potential in various applications by their high porosity and excellent oxidation resistance. However, to obtain a controllable NiAl intermetallic structure by tuning different process parameters remains unclear. In this work, porous NiAl intermetallic compounds were fabricated by economic and energy-saving thermal explosion (TE) reaction. The relationship between microstructure and process parameters was revealed using three-dimensional X-ray microscopy (3D-XRM) with high resolution and non-destructive characteristics. The geometrical features and quantitative statistics of the porous NiAl obtained at different heating rates (2, 10, 20 \ub0C min−1) were compared. The result of the closed porosity calculation showed that a lower heating rate (2 \ub0C min−1) promoted the Kirkendall reaction between Ni and Al, resulting in a high closed porosity (5.25%). However, at a higher heating rate (20 \ub0C min−1), a homogeneous NiAl phase was observed using the threshold segmentation method, indicating uniform and complete TE reaction can be achieved at a high heating rate. The result of the 3D fluid simulation showed that the sample heated at 10 \ub0C min−1 had the highest permeability (2434.6 md). In this study, we systematically investigated the relationship between the heating rates and properties of the porous NiAl intermetallic, including the phase composition, porosity, exothermic mechanism, oxidation resistance, and compression resistance. Our work provides constructive directions for designing and tailoring the performance of porous NiAl intermetallic compounds

NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task

We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin

Antibiotic-Loaded Boron Nitride Nanoconjugate with Strong Performance against Planktonic Bacteria and Biofilms

Protecting surfacesfrom biofilm formation presents a significantchallenge in the biomedical field. The utilization of antimicrobialcomponent-conjugated nanoparticles is becoming an attractive strategyagainst infectious biofilms. Boron nitride (BN) nanomaterials havea unique biomedical application value due to their excellent biocompatibility.Here, we developed antibiotic-loaded BN nanoconjugates to combat bacterialbiofilms. Antibiofilm testing included two types of pathogens, Staphylococcus aureus and Escherichiacoli. Gentamicin was loaded on polydopamine-modifiedBN nanoparticles (GPBN) to construct a nanoconjugate, which was veryeffective in killing E. coli and S. aureus planktonic cells. GPBN exhibited equallystrong capacity for biofilm destruction, tested on preformed biofilms.A 24 h treatment with the nanoconjugate reduced cell viability bymore than 90%. Our results suggest that GPBN adheres to the surfaceof the biofilm, penetrates inside the biofilm matrix, and finallydeactivates the cells. Interestingly, the GPBN coatings also stronglyinhibited the formation of bacterial biofilms. Based on these results,we suggest that GPBN could serve as an effective means for treatingbiofilm-associated infections and as coatings for biofilm prevention

Research of TE behaviour and compression property of porous Ni–Al–Cr intermetallic compounds in the β phase region

Ni–Al–Cr alloys in the β phase (B2–NiAl) region exhibit remarkable stability and mechanical property. Through thermal explosion (TE) reaction, Ni–Al–Cr intermetallic compounds with high porosity can be obtained. In this study, the focus lies on analyzing the macroscopic morphology, microstructure, phase distribution, TE behaviour, and the mechanical property of porous Ni–Al–Cr in the β phase region. Following the TE reaction, the Al-rich sintered product demonstrates a uniform phase composition and high porosity, reaching 44.39%. The vigorous TE reaction promotes the formation of interconnected pores, while the high porosity structure compromises the mechanical properties of the sample. Conversely, the Al-poor sintered product, due to a moderate TE reaction and low porosity structure, maintains its complete morphology and exhibits excellent compression resistance (yield stress reaching 538 MPa). This study offers valuable insights for the fabrication of porous Ni–Al–Cr materials with exceptional structure and performance

Graphene Oxide Attenuates Toxicity of Amyloid-β Aggregates in Yeast by Promoting Disassembly and Boosting Cellular Stress Response

Alzheimer\u27s disease (AD) is the most prevalent neurodegenerative disease, with the aggregation of misfolded amyloid-β (Aβ) peptides in the brain being one of its histopathological hallmarks. Recently, graphene oxide (GO) nanoflakes have attracted significant attention in biomedical areas due to their capacity of suppressing Aβ aggregation in vitro. The mechanism of this beneficial effect has not been fully understood in vivo. Herein, the impact of GO on intracellular Aβ42 aggregates and cytotoxicity is investigated using yeast Saccharomyces cerevisiae as the model organism. This study finds that GO nanoflakes can effectively penetrate yeast cells and reduce Aβ42 toxicity. Combination of proteomics data and follow-up experiments show that GO treatment alters cellular metabolism to increases cellular resistance to misfolded protein stress and oxidative stress, and reduces amounts of intracellular Aβ42 oligomers. Additionally, GO treatment also reduces HTT103QP toxicity in the Huntington\u27s disease (HD) yeast model. The findings offer insights for rationally designing GO nanoflakes-based therapies for attenuating cytotoxicity of Aβ42, and potentially of other misfolded proteins involved in neurodegenerative pathology

Active anti-acetylcholinesterase component of secondary metabolites produced by the endophytic fungi of Huperzia serrata

Background: An endophytic fungus lives within a healthy plant during certain stages of, or throughout, its life cycle. Endophytic fungi do not always cause plant disease, and they include fungi that yield different effects, including mutual benefit, and neutral and pathogenic effects. Endophytic fungi promote plant growth, improve the host plant's resistance to biotic and abiotic stresses, and can produce the same or similar biologically active substances as the host. Thus, endophytic fungal products have important implications in drug development. Result: Among the numerous endophytic fungi, we identified two strains, L10Q37 and LQ2F02, that have anti-acetylcholinesterase activity, but the active compound was not huperzine A. The aim of this study was to investigate the anti-acetylcholinesterase activity of secondary metabolites isolated from the endophytic fungi of Huperzia serrata . Microbial cultivation and fermentation were used to obtain secondary metabolites. Active components were then extracted from the secondary metabolites, and their activities were tracked. Two compounds that were isolated from endophytic fungi of H. serrata were identified and had acetylcholine inhibitory activities. In conclusion, endophytic fungal strains were found in H. serrata that had the same anti-acetylcholinesterase activity. Conclusion: We isolated 4 compounds from the endophytic fungus L10Q37, among them S1 and S3 are new compounds. 6 compounds were isolated from LQ2F02, all 6 compounds are new compounds. After tested anti acetylcholinesterase activity, S5 has the best activity. Other compounds' anti acetylcholinesterase activity was not better compared with huperzine A