This paper focuses on the energy efficiency of communication in small-scale sensor networks experiencing high path loss. In particular, a sensor network on the human body or BASN is considered. The energy consumption or network lifetime of a single-hop network and a multi-hop network are compared. We derive a propagation model and a radio model for communication along the human body. Using these models, energy efficiency was studied analytically for a line and a tree topology. Calculations show that single-hop communication is inefficient, especially for nodes far away from the sink. There however, multi-hop proves to be more efficient but closer to the sink hotspots arise. Based on these findings, we propose to exploit the performance difference by either introducing extra nodes in the network, i.e. dedicated relay devices, or by using a cooperative approach or by a combination of both. We show that these solutions increase the network lifetime significantly.

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In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

We tackle the problem of validating simulation models using neural networks. We propose a neural-network-based method that first learns key properties of the behaviour of alternative simulation models, and then classifies real system behaviour as coming from one of the models. We investigate the use of multi-layer perceptron and radial basis function networks, both of which are popular pattern classification techniques. By a computational experiment, we show that our method successfully allows to distinguish valid from invalid models for a multiserver queueing system.

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