A stylized model for the continuous double auction

A stylized phenomenological model for the continuous double auction is introduced. This model is equivalent to two uncoupled M/M/1 queues. The conditions for statistical equilibrium (ergodicity) are derived. The results of Monte Carlo simulations are presented on the behaviour of price differences and log-returns

THE ESTABLISHMENT OF INTENSIVE APPLE ORCHARDS IN SERBIA

Serbia at the present time grows apple on an area of 25.917 ha with an average production of 412.000 tons per year. This production is almost 2.5 fold higher than in the period of 2001-2005., which is associated with establishment of new intensive orchards, starting from 2006.Apple production was moving from the locations, typically used for traditional apple production to the regions, mostly located in the different valleys, that poses enough quantity of fresh water for drip irrigation. The new established orchards are equipped with anti-hail net preventingfruit damagesagainst hail or intensive sunlight. The most dominant cultivars are different clones of Golden Delicious, Granny Smith, Gala and Red Delicious, which are mostly grafted on M9 rootstock. Spacing between the rows is the same as in the past (3.0-3.5 m), while  distance withinthe rows is significantly reduced and now is 0,5-0,9 m, which provide 3,200-6,250 trees ha-1. Tree height reaches 2.20-3.0 m. Large and well feathered nursery trees are used for planting, which provide fast returns of high investment. “Knip” nursery trees  as 2-year-old trees with one-year old crown are preferred for establishing new orchards. After planting, light pruning is usually applied. Only lateral shoots at the tip which are too steep and too vigorous lateral shoots along the leader are removed in its base. This type of pruning, which promotes fruit bud production and early cropping, reduces vegetative growth of the tree. In the case of good development of the trees after planting ,  they can be loaded up to 40 fruits in the second growing year, providing a yield of more than 30 tons per hectare. Production in the third leaf can achieve 40-50 t ha-1 and full production, which usually started in the fourth leaf, more than 60 t ha-1can be expected depends on cultivar and growing conditions. Pruning of the mature trees means cutting of the strong watersprouts, the upright shoots and the strong terminal shoots at the top of the tree at their base, remaining only weak fruit-bearing wood. The fruit thinning is regularly applied in modern apple orchards, starting from the second growing year, in order to achieve regular yield and uniform fruit quality.. For this purpose plant growth regulators such as auxins [naphthalene acetic acid (NAA) or naphthalene acetamide (NAAm)] and cytokinin [6 - benzyladenine (BA)] are used. Recently, herbicide metamitron, as a new chemical thinners that at a low dosage reduces photosynthesis and consequently enhances fruit drop are also used. Metamitron exhibited thinning activity when applied to apple fruitlets at the 6 to 15 mm in diameter, or even later, at 20 mm. It can be applied once or twice, depend on the weather conditions in the day of application and three days after

Tainted ores and the rise of tin bronzes in Eurasia, c. 6500 years ago

The earliest tin bronze artefacts in Eurasia are generally believed to have appeared in the Near East in the early third millennium BC. Here we present tin bronze artefacts that occur far from the Near East, and in a significantly earlier period. Excavations at Pločnik, a Vinča culture site in Serbia, recovered a piece of tin bronze foil from an occupation layer dated to the mid fifth millennium BC. The discovery prompted a reassessment of 14 insufficiently contextualised early tin bronze artefacts from the Balkans. They too were found to derive from the smelting of copper-tin ores. These tin bronzes extend the record of bronze making by c. 1500 years, and challenge the conventional narrative of Eurasian metallurgical development

Community structure of copper supply networks in the prehistoric Balkans: An independent evaluation of the archaeological record from the 7th to the 4th millennium BC

Complex network analyses of many physical, biological and social phenomena show remarkable structural regularities, yet, their application in studying human past interaction remains underdeveloped. Here, we present an innovative method for identifying community structures in the archaeological record that allows for independent evaluation of the copper using societies in the Balkans, from c. 6200 to c. 3200 BC. We achieve this by exploring modularity of networked systems of these societies across an estimated 3000 years. We employ chemical data of copper-based objects from 79 archaeological sites as the independent variable for detecting most densely interconnected sets of nodes with a modularity maximization method. Our results reveal three dominant modular structures across the entire period, which exhibit strong spatial and temporal significance. We interpret patterns of copper supply among prehistoric societies as reflective of social relations, which emerge as equally important as physical proximity. Although designed on a variable isolated from any archaeological and spatiotemporal information, our method provides archaeologically and spatiotemporally meaningful results. It produces models of human interaction and cooperation that can be evaluated independently of established archaeological systematics, and can find wide application on any quantitative data from archaeological and historical record.J.G. and M.R. acknowledge the financial support from McDonald Institute for Archaeological Research, University of Cambridge, UK’s Arts and Humanities Research Council (project AH/J001406/1) and FWO Research Foundation - Flanders

Enhancing sampling in atomistic simulations of solid state materials for batteries: a focus on olivine NaFePO4_4

The study of ion transport in electrochemically active materials for energy storage systems requires simulations on quantum-, atomistic- and meso-scales. The methods accessing these scales not only have to be effective but also well compatible to provide a full description of the underlying processes. We propose to adapt the Generalized Shadow Hybrid Monte Carlo (GSHMC) method to atomistic simulation of ion intercalation electrode materials for batteries. The method has never been applied to simulations in solid-state chemistry but it has been successfully used for simulation of biological macromolecules, demonstrating better performance and accuracy than can be achieved with the popular molecular dynamics (MD) method. It has been also extended to simulations on meso-scales, making it even more attractive for simulation of battery materials. We combine GSHMC with the dynamical core–shell model to incorporate polarizability into the simulation and apply the new Modified Adaptive Integration Approach, MAIA, which allows for a larger time step due to its excellent conservation properties. Also, we modify the GSHMC method, without losing its performance and accuracy, to reduce the negative effect of introducing a shell mass within a dynamical shell model. The proposed approach has been tested on olivine NaFePO$_4$, which is a promising cathode material for Na-ion batteries. The calculated Na-ion diffusion and structural properties have been compared with the available experimental data and with the results obtained using MD and the original GSHMC method. Based on these tests, we claim that the new technique is advantageous over MD and the conventional GSHMC and can be recommended for studies of other solid-state electrode and electrolyte materials whenever high accuracy and efficient sampling are critical for obtaining tractable simulation results.MTM2013-46553-C3-1-P Iberdrola Foundation “Grants for Research in Energy and Environment 2014” ELKARTEK Programme KK-2016/00026 BES-2014-068640 BERC 2014-2017 SEV-2013-032

Multiple Single-Unit Long-Term Tracking on Organotypic Hippocampal Slices Using High-Density Microelectrode Arrays

A novel system to cultivate and record from organotypic brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows for continuous recording of electrical activity of specific individual neurons at high spatial resolution while monitoring at the same time, neuronal network activity. For the first time, the electrical activity patterns of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied during several consecutive weeks at daily intervals. An unsupervised iterative spike-sorting algorithm, based on PCA and k-means clustering, was developed to assign the activities to the single units. Spike-triggered average extracellular waveforms of an action potential recorded across neighboring electrodes, termed ‘footprints’ of single-units were generated and tracked over weeks. The developed system offers the potential to study chronic impacts of drugs or genetic modifications on individual neurons in slice preparations over extended times

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty

Spectroscopy of 34,35Si^{34,35}Si by β\beta decay: sd-fp shell gap and single-particle states

The $^{34,35}Al\beta$ decays were studied at the CERN on-line mass separator ISOLDE by $\beta-\gamma, \beta-\gamma-\gamma$ and $\beta-n-\gamma$ measurements, in order to corroborate thelow-level description of $^{34}Si$ and to obtain the first information on the level structure of the N=21 isotope $^{35}Si$. Earlier observed $\gamma$ lines in $^{34} Al$ decay were confirmed and new gamma transitions following both beta decay and $\beta$-delayed neutron emission were established. The first level scheme in $^{35}Si$, including three excited states at 910, 974 and 2168 keV, is consistent with $J^{\pi} =3/2^{-}$ and $3/2^{+}$ for the first two states respectively. Beta-decay half-life of $T_{1/2} = 38.6 (4)$ ms and beta-delayed neutron branching $P_{n}$ value $(P_{n} =41(13)$ were measured unambiguously. The significance of the single-particle energy determination at N=21, Z=14, for assessing the effective interaction in sd-fp shell-model calculations, is discussed and illustrated by predictions for different n-rich isotopes