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Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.

In recent years, professional learning networks (PLNs) and online learning communities of practices (CoPs) enabled by social media have emerged as a conduit and communal space for faculty members to engage in professional learning. This systematic review provides a current synthesis of research surrounding social media and professional development in higher education. Articles published in peer-reviewed journals between 2009 and 2019 were reviewed and 23 articles that met our selection criteria were included for further analysis and synthesis in this review. Findings suggest that research and practice on social media-supported professional learning is still in its infancy stage. Despite that social media-supported PLNs and CoPs show potential for contributing to faculty professional learning, challenges exist in sustaining faculty participation and engagement, as well as effectively navigating the social media space, especially for novice social media users. Practical implications and future research recommendations are discussed.

Motor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain. To achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations. Empirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery.

EEG signals are widely utilized in brain-computer interface (BCI) applications. However, the non-linear and non-stationary nature of EEG signals poses a challenge when dealing with variations across subjects and sessions, leading to the covariate shift problem in recognition tasks. Conventional approaches often extract vector-form features for classifying EEG signals on a per-subject and per-session basis, resulting in the loss of discriminative features and decreased recognition performance. To address this issue, this paper presents a novel cross-subject EEG feature matrix classification method that leverages the feature matrix encompassing all subjects to recognize EEG signals. The proposed method begins by aligning the EEG covariances of each subject to an identity distribution, followed by extracting a feature matrix from the aligned EEG signals. To recognize EEG signals associated with specific mental tasks, a sparse support matrix machine is employed to select discriminative features from the feature matrix and perform classification based on these selected features. To evaluate the proposed method, two publicly available benchmark datasets containing motor imagery and event-related potentials were used in experiments. Comparative analyses with state-of-the-art methods demonstrated improved recognition performance with the proposed method. Furthermore, additional ablation studies were conducted to explore the potential application prospects of the proposed method in BCI researches.

The ability to modulate nanoparticle (NP) assemblies with atomic precision is still lacking, which hinders us from creating hierarchical NP organizations with desired properties. In this work, a hierarchical fibrous (1D to 3D) assembly of Au NPs (21-gold atom, Au 21 ) is realized and further modulated with atomic precision via site-specific tailoring of the surface hook (composed of four phenyl-containing ligands with a counteranion). Interestingly, tailoring of the associated counterion significantly changes the electrical transport properties of the NP-assembled solids by two orders of magnitude due to the altered configuration of the interacting π–π pairs of the surface hooks. Overall, our success in atomic-level modulation of the hierarchical NP assembly directly evidences how the NP ligands and associated counterions can function to guide the 1D, 2D, and 3D hierarchical self-assembly of NPs in a delicate manner. This work expands nanochemists’ skills in rationally programming the hierarchical NP assemblies with controllable structures and properties. Constructing nanoparticle assemblies with atomic precision remains a major challenge in nanoscience. Here, the authors realize atomic‐level control over the 1D, 2D and hierarchical 3D assembly of Au nanoparticles by modulating the site‐specific surface ligands and associated counterions.

It has long been a challenge to dope metal nanoparticles with a specific number of heterometal atoms at specific positions. This becomes even more challenging if the heterometal belongs to the same group as the host metal because of the high tendency of forming a distribution of alloy nanoparticles with different numbers of dopants due to the similarities of metals in outmost electron configuration. Herein we report a new strategy for shuttling a single Ag or Cu atom into a centrally hollow, rod-shaped Au 24 nanoparticle, forming AgAu 24 and CuAu 24 nanoparticles in a highly controllable manner. Through a combined approach of experiment and theory, we explain the shuttling pathways of single dopants into and out of the nanoparticles. This study shows that the single dopant is shuttled into the hollow Au 24 nanoparticle either through the apex or side entry, while shuttling a metal atom out of the Au 25 to form the Au 24 nanoparticle occurs mainly through the side entry. Doping a metal nanocluster with heteroatoms dramatically changes its properties, but it remains difficult to dope with single-atom control. Here, the authors devise a strategy to dope single atoms of Ag or Cu into hollow Au nanoclusters, creating precise alloy nanoparticles atom-by-atom.

Compared with ordinary emulsions, the unique structures endow double emulsions with special functions, which makes them have broad prospects in delivering biologically active substances and replacing fats. In recent years, due to the instability of double emulsions, numerous experiments have been carried out on their preparation technology as well as stabilization methods. This review emphatically summarizes the research progress of double emulsions, including preparation techniques, factors affecting stability, stabilization mechanisms, and related applications. At present, in addition to the traditional mechanical agitation and ultrasonic emulsification, membrane emulsification, phase inversion, and microfluidic emulsification are the main research directions, all of which can improve the stability. Moreover, the stabilizer is also a crucial factor affecting the stability of the double emulsion. Hence, in this article emphasis is placed on surfactants, biopolymers, and solid particles. The applications of double emulsions in food industry can be divided into the following categories: encapsulating substances, controlling the release, and being served as fat substitutes. Furthermore, in the future, the main challenges are to commercialize the double emulsions and enhance their safety in food applications. Additionally, developing high internal phase Pickering double emulsions and O/W/O double emulsions may become the major research trends. In this review, promising and innovative fabrication techniques of double emulsions were elaborated. Surfactants, biopolymers, and solid particles were summarized to enhance the stability of double emulsions. Moreover, various applications of food‐grade double emulsions were reviewed comprehensively.

Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers’ perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education regarding factors influencing their intent and actual practice of LA approach in course design practice, based on analysis of multiple strategies such as focus group, individual, and email interviews. Most instructional designers admitted LA had great potential, but adoption was limited. Their perception, intention, and the current state of adoption are affected by individual differences, system characteristics, social influence, and facilitating conditions. Findings have imperative implications for promoting effective implementation of LA approach in higher education.

Necroptosis is a type of programmed necrosis regulated by receptor interacting protein kinase 1 （RIP1） and RIP3. Necroptosis is found to be accompanied by an overproduction of reactive oxygen species （ROS）, but the role of ROS in regulation of necroptosis remains elusive. In this study, we investigated how shikonin, a necroptosis inducer for cancer cells, regulated the signaling leading to necroptosis in glinoma cells in vitro. Treatment with shikonin （2-10 pmol/L） dose-dependently triggered necrosis and induced overproduction of intracellular ROS in rat C6 and human SHG-44, U87 and U251 glioma cell lines. Moreover, shikonin treatment dose- dependently upregulated the levels of RIP1 and RIP3 and reinforced their interaction in the glioma cells. Pretreatment with the specific RIP1 inhibitor Nec-1 （100 pmol/L） or the specific RIP3 inhibitor GSK-872 （5 pmol/L） not only prevented shikonin-induced glioma cell necrosis but also significantly mitigated the levels of intraceliular ROS and mitochondrial superoxide. Mitigation of ROS with MnTBAP （40 pmol/L）, which was a cleaner of mitochondrial superoxide, attenuated shikonin-induced glioma cell necrosis, whereas increasing ROS levels with rotenone, which improved the mitochondrial generation of superoxide, significantly augmented shikonin-caused glioma cell necrosis. Furthermore, pretreatment with MnTBAP prevented the shikonin-induced upregulation of RIP1 and RIP3 expression and their interaction while pretreatment with rotenone reinforced these effects. These findings suggest that ROS is not only an executioner of shikonin-induced glioma cell necrosis but also a regulator of RIP1 and RIP3 expression and necrosome assembly.