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With the rapidly growing demand for large-scale online education and the advent of big data, numerous research works have been performed to enhance learning quality in e-learning environments. Among these studies, adaptive learning has become an increasingly important issue. The traditional classification approaches analyze only the surface characteristics of students but fail to classify students accurately in terms of deep learning features. Meanwhile, these approaches are unable to analyze these high-dimensional learning behaviors in massive amounts of data. Hence, we propose a learning style classification approach based on the deep belief network (DBN) for large-scale online education to identify students’ learning styles and classify them. The first step is to build a learning style model and identify indicators of learning style based on the experiences of experts; then, relate the indicators to the different learning styles. We improve the DBN model and identify a student’s learning style by analyzing each individual’s learning style features using the improved DBN. Finally, we verify the DBN result by conducting practical experiments on an actual educational dataset. The various learning styles are determined by soliciting questionnaires from students based on the ILS theory by Felder and Soloman (1996) and the Readiness for Education At a Distance Indicator. Then, we utilized those data to train our DBNLS model. The experimental results indicate that the proposed DBNLS method has better accuracy than do the traditional approaches.

In the published publication [...]

Elastic polymer-based conductive composites (EPCCs) are of great potential in the field of flexible sensors due to the advantages of designable functionality and thermal and chemical stability. As one of the popular choices for sensor electrodes and sensitive materials, considerable progress in EPCCs used in sensors has been made in recent years. In this review, we introduce the types and the conductive mechanisms of EPCCs. Furthermore, the recent advances in the application of EPCCs to sensors are also summarized. This review will provide guidance for the design and optimization of EPCCs and offer more possibilities for the development and application of flexible sensors.

Antivortices have potential applications in future nano-functional devices, yet the formation of isolated antivortices traditionally requires nanoscale dimensions and near-zero magnetocrystalline anisotropy, limiting their broader application. Here, we propose an approach to forming antivortices in multiferroic ε -Fe 2 O 3 with the coalescence of misaligned grains. By leveraging misaligned crystal domains, the large magnetocrystalline anisotropy energy is counterbalanced, thereby stabilizing the ground state of the antivortex. This method overcomes the traditional difficulty of observing isolated antivortices in micron-sized samples. Stable isolated antivortices were observed in truncated triangular multiferroic ε -Fe 2 O 3 polycrystals ranging from 2.9 to 16.7 µm. Furthermore, the unpredictability of the polarity of the core was utilized as a source of entropy for designing physically unclonable functions. Our findings expand the range of antivortex materials into the multiferroic perovskite oxides and provide a potential opportunity for ferroelectric polarization control of antivortices. The creation of magnetic antivortices in two-dimensional magnets holds significant importance in spintronics. Here, the authors demonstrate the creation of stable antivortices in ε-Fe 2 O 3 polycrystals by coalescing misaligned grains controllably.

Cognitive diagnosis models (CDMs) assess the proficiency of examinees in specific skills. Online education has increased the amount of data that is available on the response behaviour of examinees. Traditional CDMs determine the state of skills by modelling information on item response results and ignoring vital response time information. In this study, a CDM, named RT-CDM, which models the condition dependence between response time and response accuracy on the speed-accuracy exchange criterion, is proposed. The model’s continuous latent trait function and response time function, used for more precise cognitive analyses, makes it a tractable, interpretable skill diagnosis model. The Markov chain Monte Carlo algorithm is used to estimate the parameters of the RT-CDM. We evaluate RT-CDM through controlled simulations and three real datasets—PISA 2015 computer-based mathematics, EdNet-KT1, and MATH—against multiple baselines, including classical CDMs (e.g., DINA/IRT), RT-extended IRT and joint models (e.g., 4P-IRT, JRT-DINA), and neural CDMs (e.g., NCD, ICD, MFNCD). Across datasets, RT-CDM consistently achieves superior predictive performance, demonstrates stable parameter recovery in simulations, and delivers stronger diagnostic interpretability by leveraging RT alongside RA.

The development of stretchable electronics could enhance novel interface structures to solve the stretchability–conductivity dilemma, which remains a major challenge. Herein, we report a nano‐liquid metal (LM)‐based highly robust stretchable electrode (NHSE) with a self‐adaptable interface that mimics water‐to‐net interaction. Based on the in situ assembly of electrospun elastic nanofiber scaffolds and electrosprayed LM nanoparticles, the NHSE exhibits an extremely low sheet resistance of 52 mΩ sq−1. It is not only insensitive to a large degree of mechanical stretching (i.e., 350% electrical resistance change upon 570% elongation) but also immune to cyclic deformation (i.e., 5% electrical resistance increases after 330 000 stretching cycles with 100% elongation). These key properties are far superior to those of the state‐of‐the‐art reports. Its robustness and stability are verified under diverse circumstances, including long‐term exposure to air (420 days), cyclic submersion (30 000 times), and resilience against mechanical damages. The combination of conductivity, stretchability, and durability makes the NHSE a promising conductor/electrode solution for flexible/stretchable electronics for applications such as wearable on‐body physiological signal detection, human–machine interaction, and heating e‐skin. The development of stretchable electronics could enhance novel interface structures to achieve electrical stability upon stretching and cyclic durability simultaneously, which remains a major challenge. Herein, authors (DOI: 10.1002/inf2.12302) reported a nano‐LM‐based highly robust stretchable electrode (NHSE) based on the in situ assembly of electrospun elastic nanofiber scaffolds and electrosprayed LM nanoparticles by mimicking the water‐to‐net interface. Without alloying or adding binder materials, the as‐prepared NHSE realises a self‐adaptable interface to achieve a super‐low resistance under high elongation and an exceptional electrical robustness upon cyclic external stimuli. The combination of conductivity, stretchability, and durability makes the NHSE a promising conductor/electrode solution for flexible/stretchable electronics for applications such as wearable on‐body physiological signal detection, human‐machine interaction, and heating e‐skin. [Correction added on 14 March 2022, after first online publication: Graphical image caption has been updated.]

A series of OA-tacrine hybrids with the alkylamine linker was designed, synthesized, and evaluated as effective cholinesterase inhibitors for the treatment of Alzheimer's disease (AD). Biological activity results demonstrated that some hybrids possessed significant inhibitory activities against acetylcholinesterase (AChE). Among them, compounds B4 (hAChE, IC 50 = 14.37 ± 1.89 nM; SI > 695.89) and D4 (hAChE, IC 50 = 0.18 ± 0.01 nM; SI = 3374.44) showed excellent inhibitory activities and selectivity for AChE as well as low nerve cell toxicity. Furthermore, compounds B4 and D4 exhibited lower hepatotoxicity than tacrine in cell viability, apoptosis, and intracellular ROS production for HepG2 cells. These properties of compounds B4 and D4 suggest that they deserve further investigation as promising agents for the prospective treatment of AD.

Flexible strain sensors play vital role in human–machine interaction. Despite their vast development, a strain sensor having broad strain sensing range, ultralow detection limit, and negligible hysteresis still remains a challenge. Herein, a liquid metal (LM)‐based stretchable resistive strain sensor, prepared by selective wetting and transferring process to attain improved compatibility between LM and polydimethylsiloxane substrate, is reported. This sensor exhibits broad strain sensing range (105%), with ultralow detection limit (0.05%), minimal hysteresis, fast response time (58 ms), and excellent repeatability. When practically demonstrated, this sensor successfully monitored various human activities, such as blink motion monitoring, voice intensity differentiation, heartbeat and wrist pulse monitoring. Moreover, when the current sensor is used in the form of smart kneecap and smart glove, it efficiently detected various human gestures, which confirms its great application potential in the fields of human health and motion monitoring, human–machine interface, and virtual reality applications. The liquid metal (LM)‐based stretchable resistive strain sensor having broad strain sensing range, ultralow detection limit, and negligible hysteresis has been developed by selective wetting and transferring process. It efficiently detects various human gestures, which confirms its great application potential in the fields of human health and motion monitoring, human–machine interface, and virtual reality applications.

The past decades have witnessed the development of many technologies based on nanoionics, especially lithium-ion batteries (LIBs). Now there is an urgent need for developing LIBs with good high-rate capability and high power. LIBs with nanostructured electrodes show great potentials for achieving such goals. However, the nature of Li-ion transport behaviors within the nanostructured electrodes is not well clarified yet. Here, Li-ion transport behaviors in Li x CoO 2 nanograins are investigated by employing conductive atomic force microscopy (C-AFM) technique to study the local Li-ion diffusion induced conductance change behaviors with a spatial resolution of ~10 nm. It is found that grain boundary has a low Li-ion diffusion energy barrier and provides a fast Li-ion diffusion pathway, which is also confirmed by our first principles calculation. This information provides important guidelines for designing high performance LIBs from a point view of optimizing the electrode material microstructures and the development of nanoionics.

Gallium-based liquid metal has gained significant attention in conformal flexible electronics due to its high electrical conductivity, intrinsic deformability, and biocompatibility. However, the fabrication of large-area and highly uniform conformal liquid metal films remains challenging. Interfacial self-assembly has emerged as a promising method, but traditional approaches face difficulties in assembling liquid metal particles. Here, we realized the multi-size universal self-assembly (MUS) for liquid metal particles with various diameters (<500 μm). By implementing a z-axis undisturbed interfacial material releasing strategy, the interference of gravitational energy on the stability of floating particles is avoided, enabling the fabrication of ultra-conformable monolayer films with large areas (>100 cm 2 ) and high floating yield (50–90%). Moreover, the films can be conformally transferred onto complex surfaces such as human skin, allowing for the fabrication of substrate-free flexible devices. This eliminates interference from traditional substrate mechanical responses, making the liquid metal e-tattoo more user-friendly.