Prebiotic synthesis of sequential peptides on the Hadean beach by a molecular engine working with nitrogen oxides as energy sources

Addressing the still open question of the prebiotic origin of sequential macromolecules (peptides, nucleic acids) on the primitive Earth, we describe a molecular engine (the primary pump), which works at ambient temperature and continuously generates, elongates and complexifies sequential peptides. This new scenario is based on a cyclic reaction sequence, whose keystep is the activation of amino acids into their N‐carboxyanhydrides (NCA) through nitrosation by NOx. This process could have taken place on tidal beaches; it requires a buffered ocean, emerged land and a nitrosating atmosphere. With the help of geochemical studies and computer simulations of atmosphere photochemistry, we show that the primitive Earth during the Hadean may have satisfied all these requirements. © 2001 Society of Chemical Industry.     

MMORPG Player actions: Network performance, session patterns and latency requirements analysis

Providing Massively Multiplayer Online Role-Playing Games (MMORPGs) is a big challenge for future mobile, IP-based networks. Understanding how the players’ actions affect the network parameters, the game platform, and the overall perceived quality is highly relevant for the purposes of game design, as well as for the networking infrastructure and network support for games. We break player actions down into discrete categories, and show that each category is distinct in terms of several key metrics. We discuss which categories of actions could be supported on current mobile devices, and present evidence in form of a user survey demonstrating the demand for such services. The starting points into the discussion include the networking, session and latency requirements for particular player actions on one side, and the players’ interest on the other. The Blizzard Entertainment’s World of Warcraft (WoW) is used as a case study.     

Methodological approaches for histamine quantification using derivatization by chloroethylnitrosourea and ELISA measurement. Part II: Optimisation of the derivatization step

In a previous paper (part I), new strategy was used for raising antibodies against hapten (< 300 Da) and the quantification of these hapten by ELISA using derivatization by chloroethyl nitrosourea (CENU). After raising antibodies against histamine, they were characterized and used for ELISA measurements. Optimal detection conditions were determined for the histamine quantification by ELISA method. The present study investigates the derivatization step of the histamine by chloroethylnitrosourea (CENU). Five factors (2 qualitative: nature of the solvent and nature of the antibodies and 3 quantitative: pH, % of solvent and time of derivatization) have been considered. Optimal reaction conditions were established by calculation of a validated model.     

Aerodynamic design and analysis of a small-scale vertical axis wind turbine

Small-scale vertical axis wind turbines are regarded today as an attractive source of green energy, still insufficiently implemented and tested. This paper presents a fast design methodology of such a VAWT, in terms of choosing its main parameters: airfoil, rotor diameter and solidity. For obtaining generated power of each considered geometrical model at different undisturbed wind velocities and rotor angular velocities two models were used — momentum and vortex-wake model, combined with experimentally measured airfoil data (airfoil lift and drag coefficients). Even though the former model is simpler, it is the most utilized model, known to provide good results in stationary working regimes. Both models still present fairly accurate and fast tools for computation and optimization, particularly useful in the phase of conceptual design. In this research, the use of the momentum model resulted in determination of the maximal power coefficient, optimal- and minimal freestream velocity for every considered VAWT model. From these output parameters, a selection of the optimal geometric model was done, and a more detailed transient analysis and flow representation around the selected solution was obtained by the vortex-wake model. The results obtained by the two used computational models coincide satisfactorily.     

Boosted manifold principal angles for image set-based recognition

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.     

Minimising conservatism in infinite-horizon LQR control

This paper studies the formulation of the constrained infinite horizon linear quadratic regulator control law (CIHLQR). Results from recent studies in this area are extended to show that conditions used in the standard formulation of the CIHLQR law are not necessary, but merely sufficient. Through the use of a novel proof it is shown that for a general SISO system with input constraints and certain conditions imposed, saturated LQR provides the same control sequence as CIHLQR. It is further shown that saturated LQR is equivalent to the CIHLQR in the case of first-order systems, subject to both state and control constraints. Finally, the region of constrained stabilisability is characterised for the case of open-loop unstable first-order systems.     

Lake Level Prediction using Feed Forward and Recurrent Neural Networks

The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .

     

Neural-net based coordinated stabilizing control for the exciterand governor loops of low head hydropower plants

This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbines are performed and the comparisons of conventional excitation-governor state-space optimal control and neural-net based control are presented. Results obtained on the nonlinear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers