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Photosynthetic chromatophore vesicles found in some purple bacteria constitute one of the simplest light-harvesting systems in nature. The overall architecture of chromatophore vesicles and the structural integration of vesicle function remain poorly understood despite structural information being available on individual constituent proteins. An all-atom structural model for an entire chromatophore vesicle is presented, which improves upon earlier models by taking into account the stoichiometry of core and antenna complexes determined by the absorption spectrum of intact vesicles in Rhodobacter sphaeroides, as well as the well-established curvature-inducing properties of the dimeric core complex. The absorption spectrum of low-light-adapted vesicles is shown to correspond to a light-harvesting-complex 2 to reaction center ratio of 3:1. A structural model for a vesicle consistent with this stoichiometry is developed and used in the computation of excitonic properties. Considered also is the packing density of antenna and core complexes that is high enough for efficient energy transfer and low enough for quinone diffusion from reaction centers to cytochrome bc(1) complexes.

Experimental research of photocatalytical oxidation (PCO) of aqueous solutions of de‐icing agents (ethylene glycol and ethylene glycol monoethyl ether) and methyl tert‐butyl ether (MTBE) was undertaken. These chemicals are water‐soluble components of jet and motor fuels accidentally disposed to the environment. Titanium dioxide (Degussa P25) under near‐UV irradiation was selected as a photocatalyst. A slightly acidic medium was preferable for the process efficiency for MTBE, whereas a neutral medium was beneficial for de‐icing agents and jet fuel aqueous extracts. TiO2 suspension fractional composition was found to be dependent on pH and the presence of organic admixtures: the minimum size of TiO2 particles at their maximum uniformity was established in an acidic medium, where the efficiency of PCO of de‐icing agents was the poorest. On the other hand, neutral and slightly acidic media, beneficial for PCO efficiency, were favourable for particle agglomeration, which indicates a minimal role for photocatalyst particle size in PCO efficiency. PCO efficiency increased with increasing MTBE and icing inhibitor concentration. The biodegradability of aqueous solutions of oxygenated additives increased as PCO proceeded. The influence of mineral additives—sulphate, calcium, ferric and manganese ions—on the process efficiency was found to be complex. Special attention was paid to energy‐saving PCO with a photocatalyst attached to buoyant glass micro‐spheres and reduced intensity of stirring of the slurry.

http://www.ester.ee/record=b1310777*est

Meteorological forcing at the air-water interface is the main determinant of the heat balance of most lakes (Edinger et al., 1968; Sweers, 1976). Year-to-year changes in the weather therefore have a major effect on the thermal characteristics of lakes. However, lakes that differ with respect to their morphometry respond differently to these changes (Gorham, 1964), with deeper lakes integrating the effects of meteorological forcing over longer periods of time. Other important factors that can influence the thermal characteristics of lakes include hydraulic residence time, optical properties and landscape setting (e.g. Salonen et al., 1984; Fee et al., 1996; Livingstone et al., 1999). These factors modify the thermal responses of the lake to meteorological forcing (cf. Magnuson et al., 2004; Blenckner, 2005) and regulate the patterns of spatial coherence (Chapter 17) observed in the different regions (Livingstone, 1993; George et al., 2000; Livingstone and Dokulil, 2001; Jarvinen et al., 2002; Blenckner et al., 2004)

This paper describes a bidirectional twisted single-phase single-stage buck-boost dc-ac converter based on an output unfolding circuit. This solution is derived by the combination of an inverting buck-boost dc-dc converter and an unfolding circuit. The operation principle, component design guidelines, along with the control approach are presented. The zero-crossing distortion problem is discussed and solved by a simple approach. The simulation and experimental results confirm all theoretical statements. Loss distribution and achievable efficiency are analyzed. Finally, the pros and cons of the proposed solution, along with the most promising application field, are analyzed and discussed in the conclusion.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.

In the European renewable energy portfolio, wind has a sizeable share in the total energy production. The Nordic and Baltic energy systems in particular are benefiting from wind energy to reach the greenhouse gas emissions reduction objectives set by the EU. The wind energy production varies with time, and this intermittent characteristic imposes a challenge for full utilization of renewable energy potential. The power system operator needs to ensure timely power supply of demand. An accurate estimation of power output from a non-dispatchable generation resource such as a wind farm is essential for the operator to ensure the supply–demand balance and adequate sizing of reserve power capacity. Existing methods of feature extraction and prediction such as linear regression often overlook the significant variations or do not utilize in the model building. However, this method misinterprets the trend in data. Understanding the properties of the variations in more details would reduce the uncertainty and significantly improve the feature extraction to aid in decision making. Furthermore, as the volume, shape and type of dataset start to increase and new methods are required to extract meaningful information from the patterns in the big data. The objective of the paper is to present a novel Ramping BehaviourAnalysis (RBAθ) model that identifies and quantifies the variations in a time-varying dataset. The variations are classified into significant and stationary events. The former refers to the significant swings beyond a set threshold range and the latter refers to the swings that are relatively within the threshold limits. The features associated to each event include start time, end time, change in magnitude, persistence of an event, angle at which the event took place and frequency of occurrences of the features. In addition, the rain-flow cycles count is extracted from the original data for each event as a sum of half cycles and full cycles. The model is validated using simulated wind power production data from a virtual wind park spread across Estonia and the results are elaborated. The spatial dynamics of the virtual windfarm are captured through localized spatial autocorrelation of the events with the geospatial locations of the turbines. The results demonstrate that RBAθprecisely and accurately identify and quantify the time varying power generation into events with subsequent features. The volume of the data is significantly reduced in the process of summarizing time series data into a series of events. Thereby RBAθcan be also used for data compression and reconstruction with minor losses. The system operators can use the proposed algorithm in operational scheduling, maintenance and investment-capacity building decisions.