In Wireless Sensor Networks (WSNs), when multiple data packets meet during routing to the sink, redundant data can be removed through data fusion, thereby reducing the amount of data transmitted, and increasing the life of the network. However, how to increase the data fusion rate as much as possible and ensure that the delay is lower than the deadline is a challenge issue. To solve this problem, a Differentiated Threshold Configuring joint Optimal Relay Selection based Data Aggregation (DTC-ORS-DA) scheme is proposed, which can significantly reduce redundant data routing and guarantee the delay for WSN. The main innovation is as follow: (1) In DTC-ORS-DA scheme, there are two thresholds: data volume threshold and time threshold. Data routing can only be performed when or of the node meet the threshold requirements, so that the data can be fully integrated to minimize the amount of data to be transmitted. More importantly, DTC-ORS-DA adopts differentiated threshold settings based on the characteristics of unbalanced energy consumption in WSNs, and sets a smaller threshold in the far sink area with sufficient energy, so that data packets can be routed quickly. And the near sink area where the energy is tight uses a larger threshold to maximize data fusion, so that the combination can make the data fusion high, the energy is effectively used, and the delay is small. (2) We propose a priority-based relay selection algorithm, which enables child nodes to dynamically select the parent node with the highest priority based on the number of data packets, waiting time, and remaining energy. In the process of routing, the probability of nodes with many data packets or long waiting time being selected as transmission relay is high, which can either increase the data fusion rate or reduce the delay. Finally, the performance comparison with Common data collection Scheme (CS) proves that, the DTC-ORS-DA scheme reduces the average delay by 10.74%-19.91%, increases the life cycle by 9.81% at most, and the energy utilization rate is increased by 6.67%-9.48%.

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<abstract><p>To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.</p></abstract>

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In this paper, relay node deployment strategies are adopted into heterogeneous network scenario. The existence of cooperative relay nodes introduces more interference in the cellular domain. Firstly, we give the hetero-interference models which have great influence on heterogeneous access performance. Furthermore, outage probability and symbol error rate formula are derived under heterogeneous relay network. On this basis, Nearest-Neighbor algorithm and fixed deployment strategy are adopted to analyze cooperative network performance. Numerical results show that the Nearest-Neighbor solution can effectively improve the cellular performance in the heterogeneous environment.