Phase transitions and topological properties of the 5/2 quantum Hall states with strong Landau-level mixing

We numerically study a 5/2 fractional quantum Hall system with even number of electrons using the exact diagonalization where both the strong Landau level (LL) mixing and a finite width of the quantum well have been considered and adapted into a screened Coulomb interaction. With the principal component analysis, we are able to recognize a compressible-incompressible phase transition in the parameter space made of the magnetic field and the quantum well width by the competition between the first two leading components of the ground states wave functions, which is consistent with the low-lying spectral feature and previous works in the odd-electron system. In addition, the presence of the subdominant third component suggests an incompressible transition occurring as the LL-mixing strength grows into a certain parameter region associated with the ZnO experiments. We further investigate the strongly LL-mixed phase in this emerging region with the Hall viscosity, wave function overlaps, and the entanglement spectra. Results show it can be well described as a particle-hole symmetrized Pfaffian state with the dual topological properties of the Pfaffian and the anti-Pfaffian states

Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table

No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).Comment: Accepted by CIKM 2023, 10 pages, 8 figures, 5 table

Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student's distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity

Multifunctional Voltage Source Inverter for Renewable Energy Integration and Power Quality Conditioning

In order to utilize the energy from the renewable energy sources, power conversion system is necessary, in which the voltage source inverter (VSI) is usually the last stage for injecting power to the grid. It is an economical solution to add the function of power quality conditioning to the grid-connected VSI in the low-voltage distribution system. Two multifunctional VSIs are studied in this paper, that is, inductive-coupling VSI and capacitive-coupling VSI, which are named after the fundamental frequency impedance of their coupling branch. The operation voltages of the two VSIs are compared when they are used for renewable energy integration and power quality conditioning simultaneously. The operation voltage of the capacitive-coupling VSI can be set much lower than that of the inductive-coupling VSI when reactive power is for compensating inductive loads. Since a large portion of the loads in the distribution system are inductive, the capacitive-coupling VSI is further studied. The design and control method of the multifunctional capacitive-coupling VSI are proposed in this paper. Simulation and experimental results are provided to show its validity

A PEG-Fmoc conjugate as a nanocarrier for paclitaxel

We report here that a simple, well-defined, and easy-to-scale up nanocarrier, PEG5000-lysyl-(α-Fmoc-ε-t-Boc-lysine)2 conjugate (PEG-Fmoc), provides high loading capacity, excellent formulation stability and low systemic toxicity for paclitaxel (PTX), a first-line chemotherapeutic agent for various types of cancers. 9-Fluorenylmethoxycarbonyl (Fmoc) was incorporated into the nanocarrier as a functional building block to interact with drug molecules. PEG-Fmoc was synthesized via a three-step synthetic route, and it readily interacted with PTX to form mixed nanomicelles of small particle size (25–30 nm). The PTX loading capacity was about 36%, which stands well among the reported micellar systems. PTX entrapment in this micellar system is achieved largely via an Fmoc/PTX π-π stacking interaction, which was demonstrated by fluorescence quenching studies and 13C-NMR. PTX formulated in PEG-Fmoc micelles demonstrated sustained release kinetics, and in vivo distribution study via near infrared fluorescence imaging demonstrated an effective delivery of Cy5.5-labled PTX to tumor sites. The maximal tolerated dose for PTX/PEG-Fmoc (MTD > 120 mg PTX/kg) is higher than those for most reported PTX formulations, and in vivo therapeutic study exhibited a significantly improved antitumor activity than Taxol, a clinically used formulation of PTX. Our system may hold promise as a simple, safe, and effective delivery system for PTX with a potential for rapid translation into clinical study

An Improved D-α-Tocopherol-Based Nanocarrier for Targeted Delivery of Doxorubicin with Reversal of Multidrug Resistance

Nanocarriers have recently emerged as an attractive platform for delivery of various types of therapeutics including anticancer agents. Previously, we developed an improved TPGS delivery system (PEG5K-VE2) which demonstrated improved colloidal stability and greater in vivo antitumor activity. Nevertheless, the application of this system is still limited by a relatively low drug loading capacity (DLC). In this study we report that incorporation of a fluorenylmethyloxycarbonyl (Fmoc) motif at the interfacial region of PEG5K-VE2 led to significant improvement of the system through the introduction of an additional mechanism of drug/carrier interaction. Doxorubicin (DOX) could be effectively loaded into PEG5K-Fmoc-VE2 micelles at a DLC of 39.9%, which compares favorably to most reported DOX nanoformulations. In addition, PEG5K-Fmoc-VE2/DOX mixed micelles showed more sustained release of DOX in comparison to the counterpart without Fmoc motif. MTT assay showed that PEG5K-Fmoc-VE2/DOX exerted significantly higher levels of cytotoxicity over DOX, Doxil as well as PEG5K-VE2/DOX in PC-3 and 4T1.2 cells. Cytotoxicity assay with NCI/ADR-RES, a drug resistant cell line, suggested that PEG5K-Fmoc-VE2 may have a potential to reverse the multidrug resistance, which was supported by its inhibition on P-gp ATPase. Pharmacokinetics (PK) and biodistribution studies showed an increased half-life in blood circulation and more effective tumor accumulation for DOX formulated in PEG5K-Fmoc-VE2 micelles. More importantly, DOX-loaded PEG5K-Fmoc-VE2 micelles showed an excellent safety profile with a MTD (~30 mg DOX/kg) that is about 3 times as much as that for free DOX. Finally, superior antitumor activity was demonstrated for PEG5K-Fmoc-VE2/DOX in both drug-sensitive (4T1.2 and PC-3) and drug-resistant (KB 8-5) tumor models compared to DOX, Doxil, and PEG5K-VE2/DOX

Conduction modulation of solution-processed two-dimensional materials

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications