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Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

The impact of emotions on health, especially stress, is receiving increasing attention. It is important to provide a non-invasive affect detection system that can be continuously monitored for a long period of time. Multi-sensor fusion strategies can better improve the performance of affect detection models, but there are also problems such as insufficient feature extraction and poor spatiotemporal feature fusion. Therefore, this study proposes a feature-level fusion method based on long short-term memory and one-dimensional convolutional neural network to extract temporal and spatial features of electrocardiogram, electromyogram, electrical activity, temperature, accelerator and response data, respectively, and then fuse them in a summation fashion for affect and stress detection. In particular, we added the tanh activation function before feature fusion, which can improve the model's performance. We used the wearable affect and stress detection dataset to train the model, which includes three different emotion states (neutral, stress, and amusement) for three-class emotion classification with accuracy and F1-scores of 87.82% and 86.68%, respectively. Due to the importance of stress, we also studied binary classification for stress detection, where neutral and amusement were combined as non-stress, with accuracy and F1-scores of 94.9% and 94.98%, respectively. The performance of the proposed model outperforms other control models and can effectively improve the performance of affect and stress detection, and promote medical care, health care and elderly care. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low throughput and energy efficiency caused by the mutual restriction of energy efficiency and spectral efficiency in massive MIMO systems and the fact that resource allocation does not consider the factors of user service QoS and the upper and lower speed limits, a resource joint optimization method based on user service QoS guarantee is proposed. The method first performs user scheduling according to service delay and channel state under the condition of equal power distribution and calculates the current system capacity, and then combines transmit antenna power and service QoS constraints to redistribute power, and corrects the system capacity, establishing the objective function for the joint optimization of the spectral efficiency and energy efficiency. An algorithm combining deep learning and Q learning is used to solve the problem, and finally, the purpose of joint optimization is achieved. The simulation shows that the joint optimization method proposed in this paper can control the timeout of user data packets more finely and, at the same time, obtain greater energy efficiency and throughput. [ABSTRACT FROM AUTHOR]

Sanjiao, a term of traditional Chinese medicine, which consists of the upper, middle, and lower jiao, is a membrane system that encompasses the zangfu organs. We propose the idea that sanjiao is a four-way membranous channels, internally connecting with the micro-membrane of the zangfu organs, extending to the skin, muscles, tendons, bones, and joints, by which water and fluid is transferred from the upper jiao to the middle jiao, then to the lower jiao, countering the transfer of blood and essence to promote the qi movement and generate health-promoting nutrition. Pulmonary interstitial fibrosis secondary to rheumatoid arthritis is characterized by disorders of both joints and the lung, and it is a condition in which healthy qi deficiency occurs, with phlegm and blood or intertwined that blocks the joints and lung micro-membrane. In the early stage of rheumatoid arthritis, external evils, such as wind, cold, and damp, build up in the joints of the tendons and bones, and prolonged early-stage symptoms would cause phlegm production and blood stasis which may obstruct the joints. Phlegm turbidity and blood stasis remain in the sanjiao membranous channels and stagnate the pulmonary micro-membrane, which may cause the pulmonary interstitial disease. The sanjiao four-way membranous channels is the pathway for joint phlegm and blood stasis flowing to the lungs, and sanjiao obstruction can aggravate phlegm and blood stagnation. The treatment is based on the principle of dredging, transforming, and regulating sanjiao, selecting herbs for regulating qi, promoting blood circulation and expelling wind, and using drugs that tonify the lung, warm the kidney, turn to qi, and regulate the state of zangfu organ. This unblocks the sanjiao membranous channels and dissipates the phlegm and blood stasis turbidity evil of the obstructed lung micro-membrane, and it can effectively treat pulmonary interstitial fibrosis secondary to rheumatoid arthritis. [ABSTRACT FROM AUTHOR]