Short-Term Wind Power Prediction Based on Encoder-Decoder Network and Multi-Point Focused Linear Attention Mechanism.

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder-decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM-Attention-LSTM, LSTM-Self_Attention-LSTM, and CNN-LSTM-Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R2, thereby validating its predictive performance.

The Role of Acupuncture Improving Cognitive Deficits due to Alzheimer's Disease or Vascular Diseases through Regulating Neuroplasticity.

Dementia affects millions of elderly worldwide causing remarkable costs to society, but effective treatment is still lacking. Acupuncture is one of the complementary therapies that has been applied to cognitive deficits such as Alzheimer's disease (AD) and vascular cognitive impairment (VCI), while the underlying mechanisms of its therapeutic efficiency remain elusive. Neuroplasticity is defined as the ability of the nervous system to adapt to internal and external environmental changes, which may support some data to clarify mechanisms how acupuncture improves cognitive impairments. This review summarizes the up-to-date and comprehensive information on the effectiveness of acupuncture treatment on neurogenesis and gliogenesis, synaptic plasticity, related regulatory factors, and signaling pathways, as well as brain network connectivity, to lay ground for fully elucidating the potential mechanism of acupuncture on the regulation of neuroplasticity and promoting its clinical application as a complementary therapy for AD and VCI.

Priority-Aware Price-Based Power Control for Co-Located WBANs Using Stackelberg and Bayesian Games.

According to the IEEE 802.15.6 standard, interference within each wireless body area network (WBAN) can be well addressed by the time division multiple access (TDMA)-based media access control (MAC) protocol. However, the inter-WBAN interference will be caused after multiple WBANs are gathered together. This paper proposes a priority-aware price-based power control (PPPC) scheme for mitigating the inter-WBAN interference. Specifically, to maximize the transmission data rate of sensors and control the aggregate interference suffered by coordinators, a Stackelberg game is established, in which the coordinators issue interference prices and the active sensors adjust their transmission power accordingly. On the other hand, since the information about the identities of the active sensors in a specific time slot is kept private, a Bayesian game is designed to model the interaction among sensors. Moreover, the timeliness and reliability of data transmission are guaranteed by designing the sensors' priority factors and setting a priority-related active probability for each sensor. At last, a power control algorithm is designed to obtain optimal strategies of game players. Simulation results show that compared with other existing schemes, the proposed scheme achieves better fairness with a comparable network sum data rate and is more energy efficient.

Investigating mobile emotional learning for children with autistic spectrum disorders.

Realizing facial expression is likely to be one of the earliest facilitators of social engagement, and it is a core deficit for individuals with autism spectrum disorder (ASD) to build social relationship. Mobile learning creates the possibility of a learning environment and visual stimuli that can adapt its informal learning contexts to improve recognizing emotions for people with ASD. This study adopted features of mobile learning to help the realization of facial expression for the ASD. The proposed 3D Complex Facial Expression Recognition (3CFER) system was developed to help the deficit in facial expressions for this population. This study therefore, explored how children with ASD performed realizing facial expression using the 3CFER system; and how the phenomena of using the learning system for people with ASD performs. Participants (n = 24, 16 males and 8 females, m = 11.3 years old) were randomly assigned to either a control or an experimental group, and were involved with the pre-and post-test sessions. The control was not engaged in the system-treatment; and the experimental undertook the system-operation. The result showed that the experimental had great improvement in realizing facial expression compared with control, and surprise and shyness were mostly easy to be identified for them. The performance of using mobile learning system was promising well. However, the informal experience of recognizing facial expression in different social contexts was meaningful learning for this population.