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Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.

Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

Due to the strong capability of building complex nonlinear mapping without involving linearization theory and high prediction efficiency; the deep learning (DL) technique applied to solve geophysical inverse problems has been a subject of growing interest. Currently, most DL-based inversion approaches are fully data-driven (namely standard deep learning), the performance of which largely depends on the training sample sets. However, due to the heavy burden of time and computational resources, it can be challenging to supply such a massive and exhaustive training dataset for generic realistic exploration scenarios and to perform network training. In this work, based on the recent advances in physics-based networks, the physical laws of magnetotelluric (MT) wave propagation is incorporated into a purely data-driven DL approach (PlainDNN) and thus builds a physics-driven DL MT inversion scheme (PhyDNN). In this scheme, the forward operator modeling MT wave propagation is integrated into the network training loop, in the form of minimizing a hybrid loss objective function composed of the data-driven model misfit and physics-based data misfit, to guide the network training. Consequently, the proposed PhyDNN method will take the advantage of the fully data-driven DL and conventional physics-based deterministic methods, allowing it to deal with complex realistic exploration scenarios. Quantitative and qualitative analysis results demonstrate that the PhyDNN can honor the physical laws of the MT inverse problem, and with other conditions unchanged, the PhyDNN outperforms the PlainDNN and the classical deterministic Occam inversion method. When processing field data, the PhyDNN method yields considerably impressive inversion results compared to the Occam method, and the corresponding simulated MT responses agree well with the real measurements, which confirms the effectiveness and applicability of the PhyDNN method.

Gallic acid is an active phenolic acid widely distributed in plants, and there is compelling evidence to prove its anti-inflammatory effects. NLRP3 inflammasome dysregulation is closely linked to many inflammatory diseases. However, how gallic acid affects the NLRP3 inflammasome remains unclear. Therefore, in the present study, we investigated the mechanisms underlying the effects of gallic acid on the NLRP3 inflammasome and pyroptosis, as well as its effect on gouty arthritis in mice. The results showed that gallic acid inhibited lactate dehydrogenase (LDH) release and pyroptosis in lipopolysaccharide (LPS)-primed and ATP-, nigericin-, or monosodium urate (MSU) crystal-stimulated macrophages. Additionally, gallic acid blocked NLRP3 inflammasome activation and inhibited the subsequent activation of caspase-1 and secretion of IL-1β. Gallic acid exerted its inhibitory effect by blocking NLRP3-NEK7 interaction and ASC oligomerization, thereby limiting inflammasome assembly. Moreover, gallic acid promoted the expression of nuclear factor E2-related factor 2 (Nrf2) and reduced the production of mitochondrial ROS (mtROS). Importantly, the inhibitory effect of gallic acid could be reversed by treatment with the Nrf2 inhibitor ML385. NRF2 siRNA also abolished the inhibitory effect of gallic acid on IL-1β secretion. The results further showed that gallic acid could mitigate MSU-induced joint swelling and inhibit IL-1β and caspase 1 (p20) production in mice. Moreover, gallic acid could moderate MSU-induced macrophages and neutrophils migration into joint synovitis. In summary, we found that gallic acid suppresses ROS generation, thereby limiting NLRP3 inflammasome activation and pyroptosis dependent on Nrf2 signaling, suggesting that gallic acid possesses therapeutic potential for the treatment of gouty arthritis.

Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition‐type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)‐long short‐term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time‐frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN‐LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short‐term forecasting performance is superior to the long‐term and medium‐term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price‐driving mechanism from the point of multiscale time‐frequency characteristics. Particularly, short‐term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions. This paper constructs a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise‐long short‐term memory, that overcomes the problem of mode mixing and larger reconstruction error for the traditional empirical mode decomposition models. The conclusion proves the superiority and robustness of the proposed machine learning model.

In this work, the amino-functionalized cellulose nanocrystal (ACNC) was prepared using a green route and applied as a biosorbent for adsorption of Cr(VI), Pb2+, and Cu2+ from aqueous solutions. CNC was firstly oxidized by sodium periodate to yield the dialdehyde nanocellulose (DACNC). Then, DACNC reacted with diethylenetriamine (DETA) to obtain amino-functionalized nanocellulose (ACNC) through a Schiff base reaction. The properties of DACNC and ACNC were characterized by using elemental analysis, Fourier transform infrared spectroscopy (FT-IR), Kaiser test, atomic force microscopy (AFM), X-ray diffraction (XRD), and zeta potential measurement. The presence of free amino groups was evidenced by the FT-IR results and Kaiser test. ACNCs exhibited an amphoteric nature with isoelectric points between pH 8 and 9. After the chemical modification, the cellulose I polymorph of nanocellulose remained, while the crystallinity decreased. The adsorption behavior of ACNC was investigated for the removal of Cr(VI), Pb2+, and Cu2+ in aqueous solutions. The maximum adsorption capacities were obtained at pH 2 for Cr(VI) and pH 6 for Cu2+ and Pb2+, respectively. The adsorption all followed pseudo second-order kinetics and Sips adsorption isotherms. The estimated adsorption capacities for Cr(VI), Pb2+, and Cu2+ were 70.503, 54.115, and 49.600 mg/g, respectively.

Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off‐state current. To this end, a crossbar device using 2D HfSe2 is fabricated, and then the top layers are oxidized into “high‐k” dielectric HfSexOy via oxygen plasma treatment, so that the cell resistance can be remarkably increased. This two‐terminal Ti/HfSexOy/HfSe2/Au device exhibits excellent forming‐free resistive switching performance with high switching speed (<50 ns), low operation voltage (<3 V), large switching window (103), and good data retention. Most importantly, the operation current and the power consumption reach 100 pA and 0.1 fJ to 0.1 pJ, much lower than other HfO based memristors. A functionally complete low‐power Boolean logic is experimentally demonstrated using the memristive device, allowing it in the application of energy‐efficient in‐memory computing. An energy‐efficient memristive device based on 2D HfSe2 oxides is fabricated, which is able to implement functionally complete Boolean logic with operation current down to 100 pA. The low‐power switching is realized by the formation and rupture of cone‐shaped O‐vacancy filaments in the ultrathin Hf−Se−O layer.

Epilepsy is one of the most prevalent neurological disorders. Its accurate detection is a challenge since sometimes patients do not experience any prior alert to identify a seizure. Electroencephalography ( EEG ) recordings are used for seizure detection, but these are usually of longer duration, and as a result, the behavior of the inherent data set is highly imbalanced. To detect seizures in such a scenario is a challenging task; using a typical classifier such as decision tree and decision forest can result in highly skewed class value (non-seizure), causing incorrect detection of epileptic patients. To solve this, a cost-sensitive learning method with a random forest was used. An algorithm that helps in seizure detection by penalizing the cost of a false negative concerning the duration of an EEG recording was proposed. The experimental results show that executing the classifier without penalty or inadequate penalties to the cost matrix is not a satisfactory solution. As a result, the algorithm provides up to 100% recall, which means all the seizure seconds are detected. The proposed method substantiates achieving higher actual seizure detection rates; the imposed penalty should be equal to the time duration of the EEG recordings (in seconds) for a patient. Hence, it can be potentially applied to the pre-consultation to the neurologist at the Outpatient Department for the actual seizure detection cases and refer them to the neurology department for further consultation.

Copper (Cu) is an essential trace element involved in a variety of biological processes, such as antioxidant defense, mitochondrial respiration, and bio-compound synthesis. In recent years, a novel theory called cuproptosis has emerged to explain how Cu induces programmed cell death. Cu targets lipoylated enzymes in the tricarboxylic acid cycle and subsequently triggers the oligomerization of lipoylated dihydrolipoamide S-acetyltransferase, leading to the loss of Fe–S clusters and induction of heat shock protein 70. Gynecological malignancies including cervical cancer, ovarian cancer and uterine corpus endometrial carcinoma significantly impact women’s quality of life and even pose a threat to their lives. Excessive Cu can promote cancer progression by enhancing tumor growth, proliferation, angiogenesis and metastasis through multiple signaling pathways. However, there are few studies investigating gynecological cancers in relation to cuproptosis. Therefore, this review discusses Cu homeostasis and cuproptosis while exploring the potential use of cuproptosis for prognosis prediction as well as its implications in the progression and treatment of gynecological cancers. Additionally, we explore the application of Cu ionophore therapy in treating gynecological malignancies.

Effective forecasting the financial distress of energy enterprises can promote risk management and financial sustainability. Previous studies lack discussion of this topic in the emerging energy industry and ignore the sample imbalance problem and information screening. Therefore, the main work of this study is constructing a multi-dimensional evaluation system that integrates financial and non-financial indicators and then designing a hybrid forecasting model of Lasso-CCSM-VNWOA-GBDT. The model integrates the least absolute shrinkage and selection operator (Lasso) regression, cluster centroids (CC) algorithm, synthetic minority over-sampling technique (SMOTE), whale optimization algorithm improved by von Neumann topology (VNWOA), and gradient boosting decision tree (GBDT). The results show that, (1) the multi-dimensional evaluation system can comprehensively assess financial distress. In particular, the non-financial indicators of audit opinions, government subsidies, and number of R&D personnel are important identified variables. (2) The Lasso regression and CCSM algorithms are superior in indicator screening and sample balancing operations, the proposed model presents the advantage of forecasting financial distress for all evaluation criteria. (3) Total liabilities, operating income, and government subsidies are the top three marginal contributions to the forecasting of financial distress. This conclusion can help new energy enterprises improve risk warnings and achieve financially healthy and sustainable development.