Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.

Gravity variation before the Akto Ms6.7 earthquake, Xinjiang

The relationship between gravity variation and the Akto Ms6.7 earthquake on November 11, 2016, was studied by use of mobile gravity observation data from the China continental structural environmental monitoring network. The result revealed that before the Akto earthquake, a high positive gravity variation was observed in the Pamir tectonic knots region （within a maximum magnitude of approximately ＋80 microgal）, which was consistent with the existing knowledge of gravity abnormality and the locations of strong earthquakes. In view of the recent strong seismic activities in the Pamir tectonic knots region, as well as the strong upward crust movement and compressive strain, it is believed that gravity change in the Pamir tectonic knots region reflects the recent strong seismic activities and crust movement.

Medical Diagnosis System Based on Fast-weights Scheme

Clinical examination data often have the features of carrying vague information,missing data and incomplete examination records,which lead to higher probabilities of misdiagnosis.A variational recursive-discriminant joint model with fast weights(FWs)scheme is proposed.MIMIC-III data sets are trained and tested,and the results are used to diagnosing.Variational recurrent neural network(VRNN)with FWs can better obtain the temporal features with partly missing data,and discriminant neural network(DNN)is for decision.Moreover,layer regularization(LN)avoids the overflow of loss function and stabilize the dynamic parameters of each layer.For the simulations,10 laboratory tests were selected to predict 10 diseases,1600 samples and 400 samples were used for training and testing,respectively.The test accuracy of disease diagnosis without FWs is 72.55%,and that with FWs is 85.80%.Simulations reveal that the FWs mechanism can effectively optimize the system model,abstracting the features for diagnose,and significantly improve the accuracy of decision-making.

Intelligent Scheduling Controller Design for Networked Control Systems Based on Estimation of Distribution Algorithm

The use of communication networks in control loops has gained increasing attention in recent years due to its advantages and flexible applications. The network quality-of-service （QoS） in those socalled networked control systems always fluctuates due to changes of the traffic load and available network resources, This paper presents an intelligent scheduling controller design approach for a class of NCSs to handle network QoS variations, The sampling period and control parameters in the controller are simultaneously scheduled to compensate for the network QoS variations. The estimation of distribution algorithm is used to optimize the sampling period and control parameters for better performance. Compared with existing networked control methods, the controller has better ability to compensate for the network QoS variations and to balance network loads. Simulation results show that the plant setting time with the intelligent scheduling controller is reduced by about 64.0% for the medium network load and 49.1% for high network load and demonstrate the effectiveness of the proposed approaches.

Dynamic data packing towards the optimization of QoC and QoS in networked control systems

A class of networked control systems is investigated whose communication network is shared with other applications. The design objective for such a system setting is not only the optimization of the control performance but also the efficient utilization of the communication resources. We observe that at a large time scale the data packet delay in the communication network is roughly varying piecewise constant, which is typically true for data networks like the Internet. Based on this observation, a dynamic data packing scheme is proposed within the recently developed packet-based control framework for networked control systems. As expected this proposed approach achieves a fine balance between the control performance and the communication utilization: the similar control performance can be obtained at dramatically reduced cost of the communication resources. Simulations illustrate the effectiveness of the proposed approach.

VG-DOCoT:a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition

Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological state.Emotions have a pronounced influence on human behavior,cognition,communication,and decision-making.However,current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical applications.To solve this problem,a novel electroencephalogram(EEG)emotion recognition network named VG-DOCoT is proposed,which is based on depthwise over-parameterized convolutional(DO-Conv),transformer,and variational automatic encoder-generative adversarial network(VAE-GAN)structures.Specifically,the differential entropy(DE)can be extracted from EEG signals to create mappings into the temporal,spatial,and frequency information in preprocessing.To enhance the training data,VAE-GAN is employed for data augmentation.A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network.A transformer structure is introduced into the network framework to reveal the global dependencies from EEG signals.Using the proposed model,a binary classification on the DEAP dataset is carried out,which achieves an accuracy of 92.52%for arousal and 92.27%for valence.Next,a ternary classification is conducted on SEED,which classifies neutral,positive,and negative emotions;an impressive average prediction accuracy of 93.77%is obtained.The proposed method significantly improves the accuracy for EEG-based emotion recognition.

Research on potential influential scholars in the field of information science and their cooperative characteristics

With the continuous development of social media,the ways and means of academic influence evaluation of scholars are increasing rapidly.The emergence of the Citation Network Structural Variation model method breaks the traditional way of identifying the influence of scholars through scientometrics index,author cooperation or node indicators in author citation network structure.Based on this method,CiteSpace software tool is used to detect scholars with potential influence in the field of Information Science and reveal the cooperative characteristics of scholars with potential influence.The study found that the most potentially influential five-pointed star scholars in the field of Information Science mainly include Leydesdorff L,Bornmann L,Thelwall M,Bar-llan J,Waltman L,Huang MH,Rousseau R and others.Pentagram scholars are usually located at the core of different cooperative groups in the author's cooperative network.Other influential non-pentagram scholars and pentagram scholars maintain a high frequency of cooperation and have a high similarity in research direction.