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Abstract: The restricted energy of nodes is one of the most important challenges in wireless sensor networks. Since data transmissions among nodes consume most of the nodes' energy, thus, minimizing the unnecessary transmissions reduces the consumed energy. One of the sources of this problem is the redundancy of raw data that can be eliminated at the aggregation points. As a result, data aggregation can be considered as an effective strategy to tackle the mentioned issue and to optimize the communication energy consumption. In this paper, the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree. For this, this paper proposes to apply a swarm intelligent algorithm named river formation dynamics. The simulation results show that the proposed algorithm outperforms in comparison to the famous ant colony optimization algorithm in terms of network lifetime. Simulations show that the proposed algorithm makes nearly 4% and 50% improvement in lifetime of wireless sensor networks than ant colony optimization and shortest path routing, respectively. [ABSTRACT FROM AUTHOR]

Summary: Recently, many researches have been conducted to exploit the compressive sensing (CS) theory in wireless sensor networks (WSNs). One of the most important goals in CS is to prolong the lifetime of WSNs. But CS may suffer from some errors during the reconstruction phase. In addition, an adaptive version of CS named Bayesian compressive sensing has been studied to improve the reconstruction accuracy in WSNs. This paper investigates these adaptive methods and identifies their associated problems. Finally, a distributed and semi‐adaptive CS‐based data collection method is proposed. The proposed method tackles the aforementioned problems. Simulation results show that considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 200% improvement compared with the Bayesian compressive sensing‐based method and outperforms other compared methods in the literature. [ABSTRACT FROM AUTHOR]

Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. [ABSTRACT FROM AUTHOR]