Experimental and Theoretical Study on Electron Ionization and Fragmentation of Propylene Oxide─the First Chiral Molecule Detected in the Interstellar Medium.

Propylene oxide, CH3CHOCH2, is the first chiral molecule detected in space and the third C3 oxide detected toward the Sagittarius B2 (Sgr B2 (N)) molecular cloud, the others being propanal, CH3CH2CHO, and acetone, (CH3)2CO. With homochirality being ubiquitous in the building blocks of living matter, the formation and decay paths of propylene oxide in space are of specific interest. Motivated by the significant role of photo- and secondary electrons in astrochemistry, we have studied electron ionization and fragmentation of propylene oxide. Ion appearance energies are determined and compared to threshold values for the respective processes calculated at the G4MP2 level of theory, and potential reaction pathways are computed at the DFT level of theory. Electron ionization is found to destabilize propylene oxide, leading to barrierless opening of the C1-C2 bond of the epoxy ring, hydrogen transfer, and fragmentation over the methyl vinyl ether or rupture of the C2-O bond of the epoxy ring and fragmentation of the allyl alcohol cation as an intermediate, rather than direct bond ruptures.

Learning the dynamics of realistic models of C. elegans nervous system with recurrent neural networks.

Given the inherent complexity of the human nervous system, insight into the dynamics of brain activity can be gained from studying smaller and simpler organisms. While some of the potential target organisms are simple enough that their behavioural and structural biology might be well-known and understood, others might still lead to computationally intractable models that require extensive resources to simulate. Since such organisms are frequently only acting as proxies to further our understanding of underlying phenomena or functionality, often one is not interested in the detailed evolution of every single neuron in the system. Instead, it is sufficient to observe the subset of neurons that capture the effect that the profound nonlinearities of the neuronal system have in response to different stimuli. In this paper, we consider the well-known nematode Caenorhabditis elegans and seek to investigate the possibility of generating lower complexity models that capture the system's dynamics with low error using only measured or simulated input-output information. Such models are often termed black-box models. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state-of-the-art recurrent neural network architectures such as Long Short-Term Memory and Gated Recurrent Units and compare these architectures in terms of their properties and their accuracy (Root Mean Square Error), as well as the complexity of the resulting models. We show that Gated Recurrent Unit models with a hidden layer size of 4 are able to accurately reproduce the system response to very different stimuli. We furthermore explore the relative importance of their inputs as well as scalability to more scenarios.

An Intelligent Weather Station.

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.