Mapping the virtual water trade in water-scarce basin: an environmentally extended input-output analysis in the Yellow River Basin of China.

China's Yellow River, the nation's second-longest, grapples with severe water scarcity, impeding the high-quality development of its basin. Our study meticulously examines the intricate virtual water trade network inside and outside the basin, providing essential insights to combat its acute water scarcity. We calculated water consumption coefficients for seven pivotal sectors across diverse Chinese provinces, forming the foundational data for quantifying virtual water trade both inside and outside the basin.

An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.

Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction.

Strong Generalized Speech Emotion Recognition Based on Effective Data Augmentation.

The absence of labeled samples limits the development of speech emotion recognition (SER). Data augmentation is an effective way to address sample sparsity. However, there is a lack of research on data augmentation algorithms in the field of SER.

Cross-Corpus Speech Emotion Recognition Based on Transfer Learning and Multi-Loss Dynamic Adjustment.

In this paper, we do research on cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from different speech corpus. The mismatched feature distribution between the training and testing sets makes many classical algorithms unable to achieve better results. To deal with this issue, a transfer learning and multi-loss dynamic adjustment (TLMLDA) algorithm is initiatively proposed in this paper.

Multi-Stream Convolution-Recurrent Neural Networks Based on Attention Mechanism Fusion for Speech Emotion Recognition.

The quality of feature extraction plays a significant role in the performance of speech emotion recognition. In order to extract discriminative, affect-salient features from speech signals and then improve the performance of speech emotion recognition, in this paper, a multi-stream convolution-recurrent neural network based on attention mechanism (MSCRNN-A) is proposed. Firstly, a multi-stream sub-branches full convolution network (MSFCN) based on AlexNet is presented to limit the loss of emotional information.

Improved semi-supervised autoencoder for deception detection.

Existing algorithms of speech-based deception detection are severely restricted by the lack of sufficient number of labelled data. However, a large amount of easily available unlabelled data has not been utilized in reality. To solve this problem, this paper proposes a semi-supervised additive noise autoencoder model for deception detection.