1,002 research outputs found
Synthesis and Characterisation of Degradable Thermosetting Materials
Full text linkAbstract
Traditional thermosetting materials generally display good durability, yet poor tractability, reworkability, and degradability. This project, however, provides a class of thermoset ring-opening metathesis polymerization (ROMP) materials based on norbornene dicarboximide moieties containing acetal ester group linkage which is degradable when subjected to heat or acidic-catalysis.
In this study, acetal ester linkages were introduced into di-functional monomer by a one-step neat reaction between a functionalised imidonorbornene containing a terminal carboxylic acid group and a 1,4-butanediol divinyl ether. Each monomer and product was characterised by 1H and 13C NMR analysis, while the obtained polymers were analysised by thermogravimetric analysis (TGA), FTIR, dynamic mechanical analysis (DMA) and oven. The results of TGA indicated that the cross-linked materials started losing weight at 150℃ and the extent of the weight loss at 300℃. The IR spectra as it showed the reduction in the intensity of acetal ester band not the complete disappearance. The samples were heated in the oven at 300℃ and 250℃. The results showed the higher the DFM content of the cross-linked materials and the heating temperature and the duration of the heating. 1H NMR analysis of cross-linked sample C11, after the heating treatment in the oven at 300℃ for 2hr, indicates the partial formation of linear polymer upon heating. The cross-linked materials were also subjected to acid-catalysed hydrolysis. The samples after hydrolysis in dilute acid were completely soluble in DCM and were therefore characterised by NMR, which shows all the acetal ester linkages were broken down during hydrolysis and that cross-linked polymers changed into linear polymers. Dynamic mechanical analysis was carried out on the cross-linked polymer, linear polymer, polymer after heating, and polymer after hydrolysis, which support that the crosslinking in the polymer were breakdown during heating, but a new kind of network was formed, and the complete breakdown of actela ester linkages after hydrolysis
Real-Time Gaze Awareness in Conversational Agents:Enhancing Collaboration and Personalization in VR Art Experiences
Full text linkThe increasing integration of conversational agents into interactive environments has prompted the investigation of methods to enhance user experience. This study explores how varying levels of real-time gaze awareness influence interactions with LLM-based conversational agents within a Virtual Reality (VR) art exhibition. Fifty-one participants were randomly assigned to one of three experimental groups, each experiencing a VR exhibition featuring five paintings and interacting with a conversational agent. The groups differed based on the agent’s gaze-awareness capabilities: 1) identifying the painting the user was viewing, 2) responding based on specific areas within a painting, and 3) analyzing detailed gaze patterns, including fixation counts, area transitions, and dwell times. Results revealed that participants in the third group, where detailed gaze pattern analysis was employed, perceived their interactions as significantly more personalized and collaborative. However, this did not lead to measurable differences in reported enjoyment, engagement, or perceived gaze awareness compared to the other conditions
sj-pdf-1-imr-10.1177_03000605221106410 - Supplemental material for Ureaplasma urealyticum induces polymorphonuclear elastase to change semen properties and reduce sperm motility: a prospective observational study
No full textSupplemental material, sj-pdf-1-imr-10.1177_03000605221106410 for Ureaplasma urealyticum induces polymorphonuclear elastase to change semen properties and reduce sperm motility: a prospective observational study by Huang Liu, Xiaoyan Song, Mulan Huang, Huashen Zhan, Shiyang Wang, Shenghui Zhu, Tao Pang, Xinzong Zhang and Qingqi Zeng in Journal of International Medical Research</p
Three-dimensional impact-time-constrained proportional navigation guidance using range-varying gain
No full textThis paper presents a novel feedback guidance law to address the impact time control problem of homing missiles in three-dimensional (3D) space. First, a range-varying gain is incorporated into the conventional proportional navigation guidance (PNG) law, which leads to globally precise time-to-go solution and achievable impact time region. Next, an impact time control guidance (ITCG) law is developed by augmenting the proposed varying-gain PNG law with a feedback biased term. Unlike most existing studies, the proposed ITCG law does not involve any linearized approximations or numerical iterative schemes. Furthermore, it can achieve the desired impact time and high-precision interception for a wide launch envelop with arbitrary initial leading angles. Finally, extensive numerical simulations, including comparison studies and Monte Carlo assessments, are carried out to validate the theoretical findings. & COPY; 2023 Elsevier Masson SAS. All rights reserved.
Co-attention-Based Pairwise Learning for Author Name Disambiguation
Full text linkDigital libraries face a pressing issue of author name ambiguity. This paper proposes a novel pairwise learning model for author name disambiguation, utilizing self-attention and co-attention mechanisms. The model integrates textual, discrete, and co-author attributes, amongst others, to capture comprehensive information from bibliographic records. It incorporates an optional random projection-based dimension reduction technique for efficiency to handle large datasets. The attention weight visualizations provide explanations for the model’s predictions. Our experiments on a substantial bibliographic catalogue repository validate the model’s effectiveness using accuracy, F1, and ROC AUC scores.</p
Additional file 1: Figure S1. of The prescriptions from Shenghui soup enhanced neurite growth and GAP-43 expression level in PC12 cells
No full textThe TQSS and EPSI drugs suppressed the viability of PC12 cells. (A) The viability of PC12 cells was measured with MTT after incubated for 48 h with TQSS derived from Shenghui soup at different concentrations (10, 20, 50, 100, 200, 500 and 2000 mg/L). (B) The relative viability of PC12 cells treated with different concentrations (5, 10, 20, 50, 100, 200 and 500 mg/L) of EPSI derived from Shenghui soup for 48 h. Data are expressed as mean ± SD, n = 3, **p < 0.01, ***p < 0.001 versus control without drugs. Figure S2. Little apoptosis of PC12 cells treated with TQSS and EPSI occurred. (A) The representative TUNEL pictures of PC12 cells detected by flow cytometry. The normal group and control group represent the cells cultured with 15 % serum DMEM and 1.5 % serum DMEM, respectively. The TQSS and EPSI groups stand for the apoptosis rates of PC12 cells treated with 1000 mg/L TQSS and 500 mg/L EPSI in 1.5 % serum DMEM, respectively. (B) The statistical analysis for control group, normal group TQSS and EPSI groups Data are expressed as mean ± SD, n = 3, ***p < 0.001 versus normal. (PDF 152 kb
Embracing Diversity: A Multi-Perspective Approach with Soft Labels
No full textIn subjective tasks like stance detection, diverse human perspectives are often simplified into a single ground truth through label aggregation i.e. majority voting, potentially marginalizing minority viewpoints. This paper presents a Multi-Perspective framework for stance detection that explicitly incorporates annotation diversity by using soft labels derived from both human and large language model (LLM) annotations. Building on a stance detection dataset focused on controversial topics, we augment it with document summaries and new LLM-generated labels.
We then compare two approaches: a baseline using aggregated hard labels, and a multi-perspective model trained on disaggregated soft labels that capture annotation distributions. Our findings show that multi-perspective models consistently outperform traditional baselines (higher F1-scores), with lower model confidence, reflecting task subjectivity. This work highlights the importance of modeling disagreement and promotes a shift toward more inclusive, perspective-aware NLP systems
Reinforcement learning-based generative models for spike prediction in cognitive neural prostheses
No full textInvestigating functional relationship between medial prefrontal cortex and primary motor cortex during learning by point process models
No full textAbout 15% of the world's population lives with some form of disability. The invasive brain-machine interfaces (iBMIs) interpret brain signals into corresponding functions by advanced signal processing methods, which can be a promising solution to help disabled people. Since the brain has multiple cortical regions contributing to a single function, it is essential to have a better understanding of the relationship among regions to develop better iBMIs. We are interested in modeling the functional relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are both actively involved in motor control. However, how the information is conveyed in spike trains between two regions has not been fully revealed by computational models. We investigate the co-activation between mPFC and M1 by utilizing point process models with different nonlinear capacities. Sprague Dawley (SD) rats with microelectrode implanted in both areas were trained to learn a new behavior task. Neural spike data was recorded during the learning procedure. The general linear model, the second-order general Laguerre Volterra model, and the staged point-process model are implemented to predict spike trains in M1 neurons from spike train input of mPFC neurons. We find that M1 neural spike trains can be well predicted from mPFC neural spikes, which indicates a highly correlated functional relationship between mPFC and M1 during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity significantly perform better than linear and 2nd order models, which shows the relationship between mPFC and M1 is highly likely to be nonlinear. Finally, we find that the models perform the best when the subjects become well trained with the new task comparing with the start and middle stage of learning. We conclude that the correlation between mPFC and M1 evolves more during task learning.</p
Intelligent Nanocoatings for Corrosion Protection of Steels
No full textIntelligent coatings, also called smart coatings, refer to coating systems capable of sensing the generation of corrosive environments, and self-responding to corrosion occurrence in demand. In this research, an intelligent coating technology based on doping of home-prepared nanocontainers pre-loaded with inhibitors- in an epoxy host coating was developed for effective corrosion protection of pipeline steel.
The performance of benzotriazole (BTA) inhibitors on preventing corrosion of an X65 pipeline steel was investigated in a bicarbonate solution. A layer of protective inhibitor film with a roughness of nano-meter scale was formed on the steel surface, inhibiting corrosion of the steel.
To determine the compatibility of nanocontainers with the host coating, multi-layered Halloysite polyelectrolyte nanocontainers were fabricated and doped in an epoxy coating. The coating containing Halloysite nanocontainers possessed enhanced corrosion resistance. The corrosion resistance of the coating was improved with the increasing content of the Halloysite nanocontainers in the coating.
To improve the ability of nanocontainers to encapsulate inhibitors BTA, a SiO2 nanoparticle based polyelectrolyte assembly was prepared as the BTA-encapsulating nanocontainers. At either low or high pH value (e.g., pH 2 or 11), the BTA was released to prevent steel from corrosion in chloride solutions. The Korsemeyer-Peppas model provided an estimation of the inhibitor-releasing rate, which served as the base for prediction of the service life of the intelligent coatings.
An intelligent coating was developed by doping the BTA-encapsulated, SiO2 nano-particle-based polyelectrolyte nanocontainers in the epoxy coating. For the pipeline steel coated with the intelligent coatings, the corrosion inhibition was time dependent upon self-releasing of the encapsulated inhibitors from the nanocontainers. With the increasing content of the BTA-encapsulated nanocontainers in the coating, both the coating resistance and corrosion resistance of the steel increase, resulting in a reduced corrosion of the steel.
Furthermore, superhydrophobic zinc nano-films were fabricated on X65 pipeline steel. The optimal condition for electrodeposition was under the current density of 100 mA/cm2 for 20 mins. The fabricated superhydrophobic, which had a water contact angle up to 158.4° ± 1.5°, possessed a satisfactory antifouling and self-cleaning ability, and provided an effective corrosion protection to the steel in a chloride solution
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