Molecular Dynamics of Neutral Polymer Bonding Agent (NPBA) as Revealed by Solid-State NMR Spectroscopy

Neutral polymer bonding agent (NPBA) is one of the most promising polymeric materials, widely used in nitrate ester plasticized polyether (NEPE) propellant as bonding agent. The structure and dynamics of NPBA under different conditions of temperatures and sample processing are comprehensively investigated by solid state NMR (SSNMR). The results indicate that both the main chain and side chain of NPBA are quite rigid below its glass transition temperature (Tg). In contrast, above the Tg, the main chain remains relatively immobilized, while the side chains become highly flexible, which presumably weakens the interaction between bonding agent and the binder or oxidant fillers and in turn destabilizes the high modulus layer formed around the oxidant fillers. In addition, no obvious variation is found for the microstructure of NPBA upon aging treatment or soaking with acetone. These experimental results provide useful insights for understanding the structural properties of NPBA and its interaction with other constituents of solid composite propellants under different processing and working conditions.National Natural Science Foundation (China) (21120102038)National Natural Science Foundation (China) (21373265)National Natural Science Foundation (China) (21003154

Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems

Cognitive diagnosis assessment is a fundamental and crucial task for student learning. It models the student-exercise interaction, and discovers the students' proficiency levels on each knowledge attribute. In real-world intelligent education systems, generalization and interpretability of cognitive diagnosis methods are of equal importance. However, most existing methods can hardly make the best of both worlds due to the complicated student-exercise interaction. To this end, this paper proposes a symbolic cognitive diagnosis~(SCD) framework to simultaneously enhance generalization and interpretability. The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters. Meanwhile, the accompanying challenge is that we need to tunnel the discrete symbolic representation and continuous parameter optimization. To address this challenge, we propose to hybridly optimize the representation and parameters in an alternating manner. To fulfill SCD, it alternately learns the symbolic tree by derivative-free genetic programming and learns the student and exercise parameters via gradient-based Adam. The extensive experimental results on various real-world datasets show the superiority of SCD on both generalization and interpretability. The ablation study verifies the efficacy of each ingredient in SCD, and the case study explicitly showcases how the interpretable ability of SCD works

4-(4-Amino-2-fluoro­phen­oxy)-7-meth­oxy­quinazolin-6-ol methanol monosolvate

In the title compound, C15H12FN3O3·CH3OH, the dihedral angle between the quinazoline ring system and the benzene ring is 81.18 (9)°. In the crystal, mol­ecules are linked by N—H⋯O and O—H⋯N hydrogen bonds, generating [10-1] chains of alternating main mol­ecules and solvent mol­ecules. Weak C—H⋯O inter­actions are also observed

Numerical Simulation on Dispersal Character of Fuel by Central HE

A fuel-air explosive (FAE) device consists of a shell (top-end cover, bottom-end cover, shell-side wall), a mixed fuel, a central pipe and a burst high-explosive (HE) charged in the central pipe.The mixed fuel is filled in a column structure and dispersed by the explosion drive of central burstHE in the central pipe. The dispersed fuel mixes with air, which produces combustible cloudwhich can be detonated. That is the fuel-air explosive (FAE). The height and ignition positionof the central HE charged column affect the fuel dispersal process. The initial stage of fueldispersal was simulated by numerical computation. The simulation result indicated that thedistribution of fuel dispersal velocity, when the central HE is ignited at the end, is not the sameas that when the central HE is ignited on the axis of the central HE simultaneously. When theratio of the column height of the central HE and that of the FAE device is 0.64~0.73, the distributionof fuel dispersal velocity has little difference when the central HE is ignited at the end of column.But, when the ratio of the column height of the central HE and that of the FAE device is 0.89,the fuel axial dispersal velocity is obviously more than that when the ratio of the column heightof the central HE and that of the FAE device is 0.64~0.73

Weikangning Therapy in Functional Dyspepsia and the Protective Role of Nrf2

Functional dyspepsia (FD) is a non-organic gastro-intestinal disorder that has a marked negative impact on quality of life. Compared with conventional pharmacological therapies, the traditional Chinese medicine weikangning (WKN) is a safe and effective treatment for FD. The present study aimed to determine the molecular mechanisms underlying the efficacy of WKN. The effect of different concentrations of WKN on the proliferation of the human gastric mucosal epithelial cell line GES-1 was assessed. The optimal WKN concentration to promote cell proliferation was determined, and this concentration was used to examine the effect of WKN compared with a domperidone-treated positive control group on the antioxidant capacity of GES-1 cells. The effect of WKN treatment on the growth and antioxidant activity of GES-1 cells was also assessed following nuclear factor erythroid 2 like 2 (Nrf2) knockdown. The optimal WKN dose for promoting cell growth was determined to be 0.025 mg/ml; at this concentra-tion the expression of the antioxidant proteins glutathione S-transferase P and superoxide dismutase 2 (SOD2) were significantly elevated (

Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning

Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records. While existing deep learning knowledge tracing (DLKT) methods have significantly improved prediction accuracy and achieved state-of-the-art results, they often suffer from a lack of interpretability. To address this limitation, current approaches have explored incorporating psychological influences to achieve more explainable predictions, but they tend to overlook the potential influences of historical responses. In fact, understanding how models make predictions based on response influences can enhance the transparency and trustworthiness of the knowledge tracing process, presenting an opportunity for a new paradigm of interpretable KT. However, measuring unobservable response influences is challenging. In this paper, we resort to counterfactual reasoning that intervenes in each response to answer \textit{what if a student had answered a question incorrectly that he/she actually answered correctly, and vice versa}. Based on this, we propose RCKT, a novel response influence-based counterfactual knowledge tracing framework. RCKT generates response influences by comparing prediction outcomes from factual sequences and constructed counterfactual sequences after interventions. Additionally, we introduce maximization and inference techniques to leverage accumulated influences from different past responses, further improving the model's performance and credibility. Extensive experimental results demonstrate that our RCKT method outperforms state-of-the-art knowledge tracing methods on four datasets against six baselines, and provides credible interpretations of response influences.Comment: ICDE'24 (fixing a few typos). Source code at https://github.com/JJCui96/RCKT. Keywords: knowledge tracing, interpretable machine learning, counterfactual reasoning, artificial intelligence for educatio

Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.Comment: 9 pages, 8 figure

MicroRNA319-mediated gene regulatory network impacts leaf development and morphogenesis in poplar

MicroRNA319 (miR319) has been implicated in leaf development in a number of plant species. Here we study the roles of miR319a and its regulated network in leaf development in poplars. Over-expression of miR319a in Populus alba × Populus glandulosa caused dwarf statures, narrow leaf blades and serrated leaf margins. The vascular bundles and bundle sheaths in transgenic leaves had more layers of cells than those in the leaves of control plants, indicating enhanced lignification in these cells. Among the 93 putative targets of miR319a predicted with the psRNATarget tool, only three genes, TCP (TEOSINTE BRANCHED1, CYCLOIDEA, and PROLIFERATING CELL NUCLEAR ANTIGEN BINDING FACTOR), were differentially expressed in the leaves of MIR319a-over-expression transgenic lines. With the RNA-seq data sets from multiple MIR319a over-expression transgenic lines, we built a three-layered gene regulatory network mediated by miR319a using Top-down graphic Gaussian model (GGM) algorithm that is capable of capturing causal relationships from transcriptomic data. The results support that miR319a primarily regulates the lignin biosynthesis, leaf development and differentiation as well as photosynthesis via miR319-MEE35/TCP4, miR319-TCP2 and miR319-TCP2-1 regulatory modules