Study of Recyclable and Repairable Dynamic Covalent Polymers for Sustainable 3D Printing Development

3D printing technology with valuable features, including cost-saving, easy access, and unlimited structure design, has attracted significant attention and been employed for production use. This technology has also been considered as a sustainable manufacturing method and quickly developed in recent years. However, the development of sustainable 3D printing is still facing challenges, especially in waste management. Thanks to the flexibility of 3D printing and diversified printing mechanisms, the big step forward can be approachable by the transformation from materials. This dissertation presents a variety of strategies designed for sustainable 3D printing development based on the combination of dynamic covalent chemistry and 3D printing technique. Dynamic covalent bonds provide polymers, including thermosets, with responsive covalent adaptable networks, allowing the materials to be reversible, leading to a recyclable material technology. Through synthesizing epoxy and polyurethane based dynamic covalent polymers as 3D printing materials, together with developing a corresponding 3D printing technique, the strength of green 3D printing is explored. In this study, OH functionalized multi-walled carbon nanotubes (MWCNTs-OH) are incorporated in the printing material to improve the mechanical property and tailor the photothermal conversion capability of the developed materials. Due to the super high strength and minuscule size of MWCNTs, they are widely used to reinforce polymer as fillers. With MWCNTs-OH incorporating, the ultimate tensile strength and Young\u27s modulus of samples both increased. In particular, young\u27s modulus of 2 wt% DTDA-PU was about 8.6 times higher than the pure sample. Furthermore, MWCNTs are functioning as an excellent photothermal converter to allow the heat triggering to be replaced with a laser light. Therefore, near-infrared (NIR) laser source is utilized as the heat source for targeting the damaged spot of printing parts precisely for in-situ repair application. After the sample is cut in half and repaired, the mechanical properties recover to 86.3% after three times NIR laser-triggered in-site repair. In addition, a creative approach of contactless supporting structure removalis explored to promote post-processing automation and reduce printing parts defective rate. Our sustainable 3D printing strategy has developed an environmentally friendly and energy-efficient technology, which paves the way toward a circular economy

Experimental Investigation on R245fa Throttling Devices under High Temperature

The experiments on mass flow rate characteristics of R245fa refrigerant flowing through throttling devices including seven capillary tubes and the electronic expansion valve were carried out under the high-temperature working conditions. By combining data analysis with flow correlations, the design basis that is applicable to R245fa throttling devices can be obtained. By comparing the experimental mass flow rate with that predicted by Jung Correlation and Kim Correlation, it can be concluded that root mean square deviations of two correlations are 3.2 % and 3.3%, respectively. The root mean square deviation for electronic expansion valve is 4.5%. The conclusions offer high-accuracy design basis for throttling devices selection of high-temperature heat pump systems using R245fa as refrigerant

Shape Anchor Guided Holistic Indoor Scene Understanding

This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene understanding. We observe that the search space constructed by current methods for proposal feature grouping and instance point sampling often introduces massive noise to instance detection and mesh reconstruction. Accordingly, we develop AncLearn to generate anchors that dynamically fit instance surfaces to (i) unmix noise and target-related features for offering reliable proposals at the detection stage, and (ii) reduce outliers in object point sampling for directly providing well-structured geometry priors without segmentation during reconstruction. We embed AncLearn into a reconstruction-from-detection learning system (AncRec) to generate high-quality semantic scene models in a purely instance-oriented manner. Experiments conducted on the challenging ScanNetv2 dataset demonstrate that our shape anchor-based method consistently achieves state-of-the-art performance in terms of 3D object detection, layout estimation, and shape reconstruction. The code will be available at https://github.com/Geo-Tell/AncRec

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Structural optimization and biological evaluation of 1,5-disubstituted pyrazole-3-carboxamines as potent inhibitors of human 5-lipoxygenase

AbstractHuman 5-lipoxygenase (5-LOX) is a well-validated drug target and its inhibitors are potential drugs for treating leukotriene-related disorders. Our previous work on structural optimization of the hit compound 2 from our in-house collection identified two lead compounds, 3a and 3b, exhibiting a potent inhibitory profile against 5-LOX with IC50 values less than 1µmol/L in cell-based assays. Here, we further optimized these compounds to prepare a class of novel pyrazole derivatives by opening the fused-ring system. Several new compounds exhibited more potent inhibitory activity than the lead compounds against 5-LOX. In particular, compound 4e not only suppressed lipopolysaccharide-induced inflammation in brain inflammatory cells and protected neurons from oxidative toxicity, but also significantly decreased infarct damage in a mouse model of cerebral ischemia. Molecular docking analysis further confirmed the consistency of our theoretical results and experimental data. In conclusion, the excellent in vitro and in vivo inhibitory activities of these compounds against 5-LOX suggested that these novel chemical structures have a promising therapeutic potential to treat leukotriene-related disorders

Over 300-km Transmission of 25 Gb/s Optical SSB NPAM-4 Signal with Electronic Dispersion Pre-compensation and Interference Mitigation

We experimentally demonstrate the interference mitigation in direct-detection of optical SSB signals with Nyquist-PAM-4. At 25 Gb/s, we achieve over 300-km and 500-km SSMF with an average BER of 2.7×10-3 (<HD-FEC) and 9.4×10-3 (<SD-FEC), respectively

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We promise to release all code & datasets for future research

Metabolomics Analysis of L-Arginine Induced Gastrointestinal Motility Disorder in Rats Using UPLC-MS After Magnolol Treatment

Background and Purpose: Magnolol, as the main active ingredient of Traditional Chinese Medicine, can significantly improve gastrointestinal motility disorders (GMD). In the present study, metabolomics was used to investigate the mechanism of magnolol improving L-arginine induced GMD in rats.Experimental Approach: SD rats were randomly divided into control group, model group and magnolol treated group. L-arginine was injected intraperitoneally in model and magnolol groups to induce GMD model. All intervention regimens were administered by oral gavage, once a day for five consecutive days. Relative gastric emptying rate and propulsive intestinal rate were measured. Metabolites in serum were analyzed based on UPLC-MS metabolomics technique.Results: Magnolol significantly promoted gastric emptying and small intestinal propulsion. Compared with the model group, the level of serotonin and L-tryptophan significantly reversed (P &lt; 0.05) and 22 metabolites reversed in the magnolol group. According to MetPA database analysis, magnolol has mainly affected 10 major metabolic pathways which were related to each other, Tryptophan metabolism is the most critical metabolic pathway associated with gastrointestinal tract.Conclusion: These findings suggest that magnolol has a significantly promoting effect on L-arginine induced gastrointestinal motility disorder in rats, the mechanism is to reduce the production of nitric oxide to weaken the function of nitric oxide relaxing the gastrointestinal smooth muscle and increase the content of serotonin to promote gastrointestinal peristalsis and motility, secretion, absorption of nutrients

Catalytic Mechanism Investigation of Lysine-Specific Demethylase 1 (LSD1): A Computational Study

Lysine-specific demethylase 1 (LSD1), the first identified histone demethylase, is a flavin-dependent amine oxidase which specifically demethylates mono- or dimethylated H3K4 and H3K9 via a redox process. It participates in a broad spectrum of biological processes and is of high importance in cell proliferation, adipogenesis, spermatogenesis, chromosome segregation and embryonic development. To date, as a potential drug target for discovering anti-tumor drugs, the medical significance of LSD1 has been greatly appreciated. However, the catalytic mechanism for the rate-limiting reductive half-reaction in demethylation remains controversial. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations, the catalytic mechanism of dimethylated H3K4 demethylation by LSD1 was characterized in details. The three-dimensional (3D) model of the complex was composed of LSD1, CoREST, and histone substrate. A 30-ns MD simulation of the model highlights the pivotal role of the conserved Tyr761 and lysine-water-flavin motif in properly orienting flavin adenine dinucleotide (FAD) with respect to substrate. The synergy of the two factors effectively stabilizes the catalytic environment and facilitated the demethylation reaction. On the basis of the reasonable consistence between simulation results and available mutagenesis data, QM/MM strategy was further employed to probe the catalytic mechanism of the reductive half-reaction in demethylation. The characteristics of the demethylation pathway determined by the potential energy surface and charge distribution analysis indicates that this reaction belongs to the direct hydride transfer mechanism. Our study provides insights into the LSD1 mechanism of reductive half-reaction in demethylation and has important implications for the discovery of regulators against LSD1 enzymes

GeodesicEmbedding (GE): a high-dimensional embedding approach for fast geodesic distance queries

In this paper, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the Euclidean distance in the high-dimensional space can induce the geodesic distance in the original manifold surface. However, directly solving the high-dimensional embedding problem is not feasible due to the large number of variables and the fact that the embedding problem is highly nonlinear. We overcome the challenges with two novel ideas. First, instead of taking all vertices as variables, we embed only the saddle vertices, which greatly reduces the problem complexity. We then compute a local embedding for each non-saddle vertex. Second, to reduce the large approximation error resulting from the purely Euclidean embedding, we propose a cascaded optimization approach that repeatedly introduces additional embedding coordinates with a non-Euclidean function to reduce the approximation residual. Using the precomputation data, our approach can determine the geodesic distance between any two vertices in near-constant time. Computational testing results show that our method is more desirable than previous geodesic distance queries methods