EEG-based emotion recognition using random Convolutional Neural Networks

Emotion recognition based on electroencephalogram (EEG) signals is helpful in various fields, including medical healthcare. One possible medical application is to diagnose emotional disorders in patients. Humans tend to work and communicate efficiently when in a good mood. On the other hand, negative emotions can harm physical and mental health. Traditional EEG-based methods usually extract time-domain and frequency-domain features before classifying them. Convolutional Neural Networks (CNN) enables us to extract features and classify them end-to-end. However, most CNN methods use backpropagation to train their models, which can be computationally expensive, primarily when a complex model is used. Inspired by the successes of Random Vector Functional Link and Convolutional Random Vector Functional Link, we propose using a randomized CNN model for emotion recognition that removes the need for a backpropagation method. Also, we expand our randomized CNN method to a deep and ensemble version to improve emotion recognition performance. We do experiments on the commonly used publicly available Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to evaluate our randomized CNN models. Results on the DEAP dataset show our models outperform all other models, with at least 95% accuracy for all subjects. Our ensemble version outperforms our shallow version, winning the shallow version in most subjects

Inpatient Discharges Forecasting for Singapore Hospitals by Machine Learning

Hospitals can predetermine the admission rate and facilitate resource allocation based on valid emergency requests and bed capacity estimation. The excess unoccupied beds can be determined with the help of forecasting the number of discharged patients. Extracting predictive features and mining the temporal patterns from historical observations are crucial for accurate and reliable forecasts. Machine learning algorithms have demonstrated the ability to learn temporal knowledge and make predictions for unseen inputs. This paper utilizes several machine learning algorithms to forecast the inpatient discharges of Singapore hospitals and compare them with statistical methods. A novel ensemble deep learning algorithm based on random vector functional links is established to predict inpatient discharges. The ensemble deep learning framework is optimized in a greedy layer-wise fashion. Several forecasting metrics and statistical tests are utilized to demonstrate the proposed method's superiority. The proposed algorithm statistically outperforms the benchmark with a ranking of 1.875. Finally, practical implications and future directions are discussed

Transformation Induced Plasticity Effects of a Non-Equal Molar Co-Cr-Fe-Ni High Entropy Alloy System

Abstract: Metastability-engineering strategy is an important topic for high entropy alloys (HEAs), owing to the transformation-induced plasticity effect (TRIP). In this work, TRIP effects of Co-Cr-Fe-Ni HEAs are investigated. Results indicate the tensile deformation-induced martensitic transformation occurs in Co35Cr25Fe40−xNix (x = 0–15 at %) HEAs. The excellent combination of tensile strength (760 MPa–1000 MPa) and elongation (65–35%) owe to solid solution strengthening of Co and Cr, and the TRIP effect. In non-equal molar Co-Cr-Fe-Ni systems, with the decrease of Ni content, the values of stacking fault energy (SFE) decrease; thus, TRIP phenomena occurs. Based on the experimental investigation in three different regions of the Co-Cr-Fe-Ni multicomponent phase diagram, the face-centered cubic structured Co-Cr-Fe-Ni HEAs with VEC of ~8.0 is more metastable, and TRIP phenomena are more likely to occur

Circular RNA CREBBP modulates cartilage degradation by activating the Smad1/5 pathway through the TGFβ2/ALK1 axis

Abstract Osteoarthritis, characterized by articular cartilage degradation, is the leading cause of chronic disability in older adults. Studies have indicated that circular RNAs are crucial regulators of chondrocyte development and are involved in the progression of osteoarthritis. In this study, we investigated the function and mechanism of a circular RNA and its potential for osteoarthritis therapy. The expression levels of circCREBBP, screened by circular RNA sequencing during chondrogenic differentiation in adipose tissue-derived stem cells, and TGFβ2 were significantly increased in the cartilage of patients with osteoarthritis and IL-1β-induced chondrocytes. circCREBBP knockdown increased anabolism in the extracellular matrix and inhibited chondrocyte degeneration, whereas circCREBBP overexpression led to the opposite effects. Luciferase reporter assays, rescue experiments, RNA immunoprecipitation, and RNA pulldown assays confirmed that circCREBBP upregulated TGFβ2 expression by sponging miR-1208, resulting in significantly enhanced phosphorylation of Smad1/5 in chondrocytes. Moreover, intra-articular injection of adeno-associated virus-sh-circCrebbp alleviated osteoarthritis in a mouse model of destabilization of the medial meniscus. Our findings reveal a critical role for circCREBBP in the progression of osteoarthritis and provide a potential target for osteoarthritis therapy