The technology development of power electronics and battery empowers electric vehicles as practical approaches for the compensation of instantaneous power flow imbalance. To utilize the response ...capability of distributed vehicles and stabilize the grid frequency caused by instant power mismatch, a process model hybridizing the data-driven and conventional modes is designed for the electrical-grid-electric-vehicle system. Due to the real-time variations of power system, electric vehicles, and renewable energies, the hybrid process model directly interacts with the system feedback to remove the sophisticated model establishment, which effectively establishes the mapping relationship between system states and intelligently processing demands. Based on the hybrid process model, the smart policy network with an adversarial mechanism is then developed to enhance the model behavior. To fulfill the continuous action requirement and speed up the policy convergence, a proximal optimization strategy is further introduced to adjust hyperparameters through a stochastic ratio automatically. Bridging the huge volume of real-time dynamics and the action domain, the sufficient utilization of various operation states is achieved in the proposed process model and policy network, which notably detects the optimal operation point and reduces the power flow imbalance. The developed hybrid process model and smart policy network are validated through an electrical-grid-electric-vehicle system by comprehensive studies from four aspects of the process performance, generalization, robustness, and efficiency. Compared with the linear and naive hybrid process methods, 3.95 times superior integration performance and the improved evolution process of 64.5% improvement are both demonstrated as well as the robustness facing various circumstances.
•A hybrid process model is developed to evolve the vehicle-grid process center.•A smart policy network is deployed to handle large-scale data of electric vehicles.•An adversarial neural network is introduced to assist the effective policy update.•Proximal optimization is employed for the network gradient updating.•Effectiveness, generalization, robustness, efficiency are verified facing imbalance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Graph embedding (GE) aims to acquire low-dimensional node representations while maintaining the graph’s structural and semantic attributes. Intelligent tutoring systems (ITS) signify a noteworthy ...achievement in the fusion of AI and education. Utilizing GE to model ITS can elevate their performance in predictive and annotation tasks. Current GE techniques, whether applied to heterogeneous or dynamic graphs, struggle to efficiently model ITS data. The GEs within ITS should retain their semidynamic, independent, and smooth characteristics. This article introduces a heterogeneous evolution network (HEN) for illustrating entities and relations within an ITS. Additionally, we introduce a temporal extension graph neural network (TEGNN) to model both evolving and static nodes within the HEN. In the TEGNN framework, dynamic nodes are initially improved over time through temporal extension (TE), providing an accurate depiction of each learner’s implicit state at each time step. Subsequently, we propose a stochastic temporal pooling (STP) strategy to estimate the embedding sets of all evolving nodes. This effectively enhances model efficiency and usability. Following this, a heterogeneous aggregation network is devised to proficiently extract heterogeneous features from the HEN. This network employs both node-level and relation-level attention mechanisms to craft aggregated node features. To emphasize the superiority of TEGNN, we perform experiments on several real ITS datasets and show that our method significantly outperforms the state-of-the-art approaches. The experiments validate that TE serves as an efficient framework for modeling temporal information in GE, and STP not only accelerates the training process but also enhances the resultant accuracy.
Blended learning, as an efficient teaching mode that combines the advantages of both online and offline learning, has been widely applied in universities. Nevertheless, the different learning ...patterns induce difficulty in evaluating the learning quality. In this paper, an approach of integrating online and offline interactions is proposed by constructing a weighted multiplex network (WMN), in which online communication behavior and offline peer relations are represented as edges in respective network layers, and edge weight depends on the frequency of interactions. Under the framework of WMNs, learners’ attributions such as behavior, sentiment and cognition can be systematically analyzed. We use a case study to compare the differences in various indicators between the online and offline networks, and investigate the relationships between network structure and individual sentiment, cognition and grade, respectively. Results show that the correlations between network centrality and cognition or grade are significantly improved in the WMN, which demonstrate WMNs have natural advantages in the analysis of blended learning. This study provides methodological and practical implications for the analysis and understanding of learner multiple interactions, which might contribute to improving the dynamic regulation and accurate guidance of blended learning processes and optimizing existing teaching models.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A suspended AlGaN/GaN high electron mobility transistor (HEMT) sensor with a tungsten trioxide (WO 3 ) nanofilm modified gate was microfabricated and characterized for ppm-level acetone gas ...detection. The sensor featured a suspended circular membrane structure and an integrated microheater to select the optimum working temperature. High working temperature (300°C) increased the sensitivity to up to 25.7% and drain current change <inline-formula> <tex-math notation="LaTeX">{I}_{{\text {DS}}} </tex-math></inline-formula> to 0.31 mA for 1000-ppm acetone in dry air. The transient characteristics of the sensor exhibited stable operation and good repeatability at different temperatures. For 1000-ppm acetone concentration, the measured response and recovery times reduced from 148 and 656 to 48 and 320 s as the temperature increased from 210 °C to 300 °C. The sensitivity to 1000-ppm acetone gas was significantly greater than the sensitivity to ethanol, ammonia, and CO gases, showing low cross-sensitivity. These results demonstrate a promising step toward the realization of an acetone sensor based on the suspended AlGaN/GaN HEMTs.
The automated generation of geometry proof problems represents a burgeoning research domain in the realm of artificial intelligence, with significant practical implications for mathematics education. ...In this study, we present a method for automatically generating fresh geometry proof questions by adapting existing ones. The core of the approach is a novel representation model for geometry proof problems, which we term “point geometry identity". According to this model, the premises and conclusion to be proven in a geometry proof problem, as well as their relationships, can be expressed as an expression. Subsequently, by applying equivalent deformation principles of algebraic expressions, we can modify the premises and conclusions of existing problems to generate new ones. Experimental analysis and expert evaluations indicate that our approach can efficiently generate a substantial number of diverse problems suitable for mathematics education within a short span of time. Moreover, under the same input conditions, our method can generate novel problems that existing techniques cannot.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The COVID-19 pandemic has highlighted the need for rapid and sensitive detection of SARS-CoV-2. Here, we report an ultrasensitive SARS-CoV-2 immunosensor by integration of an AlGaN/GaN ...high-electron-mobility transistor (HEMT) and anti-SARS-CoV-2 spike protein antibody. The AlGaN/GaN HEMT immunosensor has demonstrated the capability to detect SARS-CoV-2 spike proteins at an impressively low concentration of 10−22 M. The sensor was also applied to pseudoviruses and SARS-CoV-2 ΔN virions that display the Spike proteins with a single virion particle sensitivity. These features validate the potential of AlGaN/GaN HEMT biosensors for point of care tests targeting SARS-CoV-2. This research not only provides the first HEMT biosensing platform for ultrasensitive and label-free detection of SARS-CoV-2.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack ...generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues of unobservable cognitive states and an algorithmic explosion in searching. A deep learning model is initially employed to determine the learner's cognitive state and assess the feature importance. Symbolic regression algorithms are then used to parse the neural network model into algebraic equations. Experimental results show that the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings.
Remote photoplethysmography (rPPG), which uses a facial video to measure skin reflection variations, is a contactless method for monitoring human cardiovascular activity. Due to its simplicity, ...convenience and potential in large-scale application, rPPG has gained more attention over the decade. However, the accuracy, reliability, and computational complexity have not reached the expected standards, thus rPPG has a very limited application in the educational field. In order to alleviate this issue, this study proposes an rPPG-based learning fatigue detection system, which consists of the following three modules. First, we propose an rPPG extraction module, which realizes real-time pervasive biomedical signal monitoring. Second, we propose an rPPG reconstruction module, which evaluates heart rate using a hybrid of 1D and 2D deep convolutional neural network approach. Third, we propose a learning fatigue classification module based on multi-source feature fusion, which classifies a learner’s state into non-fatigue and fatigue. In order to verify the performance, the proposed system is tested on a self-collected dataset. Experimental results demonstrate that (i) the accuracy of heart rate evaluation is better than the cutting-edge methods; and (ii) based on both the subject-dependent and independent cross validations, the proposed system succeeded in not only learning person-independent features for fatigue detection but also detecting early fatigue with very high accuracy.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In recent years, it has been proposed that G9a/EZH2 dual inhibition is a promising cancer treatment strategy. Herein, we present the discovery of G9a/EZH2 dual inhibitors that merge the ...pharmacophores of G9a and EZH2 inhibitors. Among them, the most promising compound 15h displayed potent inhibitory activities against G9a (IC50 = 2.90 ± 0.05 nM) and EZH2 (IC50 = 4.35 ± 0.02 nM), superior antiproliferative profiles against RD (CC50 = 19.63 ± 0.18 μM) and SW982 (CC50 = 19.91 ± 0.50 μM) cell lines. In vivo, 15h achieved significant antitumor efficacy in a xenograft mouse model of human rhabdoid tumor with a tumor growth inhibitory rate of 86.6% without causing observable toxic effects. The on-target activity assays illustrated that compound 15h can inhibit tumor growth by specifically inhibiting EZH2 and G9a. Therefore, 15h is a potential anticancer drug candidate for the treatment of malignant rhabdoid tumor.
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