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The future trajectory of greenhouse gas concentrations depends on interactions between climate and the biogeosphere. Thawing of Arctic permafrost could release significant amounts of carbon into the atmosphere in this century. Ancient Ice Complex deposits outcropping along the ~7,000-kilometre-long coastline of the East Siberian Arctic Shelf (ESAS), and associated shallow subsea permafrost, are two large pools of permafrost carbon, yet their vulnerabilities towards thawing and decomposition are largely unknown. Recent Arctic warming is stronger than has been predicted by several degrees, and is particularly pronounced over the coastal ESAS region. There is thus a pressing need to improve our understanding of the links between permafrost carbon and climate in this relatively inaccessible region. Here we show that extensive release of carbon from these Ice Complex deposits dominates (57 ± 2 per cent) the sedimentary carbon budget of the ESAS, the world’s largest continental shelf, overwhelming the marine and topsoil terrestrial components. Inverse modelling of the dual-carbon isotope composition of organic carbon accumulating in ESAS surface sediments, using Monte Carlo simulations to account for uncertainties, suggests that 44 ± 10 teragrams of old carbon is activated annually from Ice Complex permafrost, an order of magnitude more than has been suggested by previous studies. We estimate that about two-thirds (66 ± 16 per cent) of this old carbon escapes to the atmosphere as carbon dioxide, with the remainder being re-buried in shelf sediments. Thermal collapse and erosion of these carbon-rich Pleistocene coastline and seafloor deposits may accelerate with Arctic amplification of climate warming.

Two-dimensional photon echo in the photosystem II reaction center reveals the exciton-vibrational coherences that promote directed energy/electron transfers.

A simple and fast, but sensitive TLD method for the measurement of energy and homogeneity of therapeutically used electron beams has been developed and tested. This method is based on the fact that when small thicknesses of high-Z absorbers such as lead are interposed in the high-energy electron beams, the transmitted radiation increases with the energy of the electron beams. Consequently, the ratio of readouts of TLDS held on the two sides of a lead plate varied sharply (by factor of 70) with a change in energy of the electron beam from 5 MeV to 18 MeV, offering a very sensitive method for the measurement of the energy of electron beams. By using the ratio of TL readouts of two types of TLD ribbon with widely different sensitivities, LiF TLD-700 ribbons on the upstream side and highly sensitive CaF2:Dy TLD-200 ribbons on the downstream side, an electron energy discrimination of better than +/- 0.1 MeV could be achieved. The homogeneity of the electron beam energy and the absorbed dose was measured by using a jig in which the TLDS were held in the desired array on both sides of a 4 mm thick lead plate. The method takes minimal beam time and makes it possible to carry out measurements for the audit of the quality of electron beams as well as for intercomparison of beams by mail.

This paper presents a mixed-integer model for the hourly energy and reserve scheduling of a price-taker and closed-loop pumped-storage hydropower plant operating in hydraulic short-circuit mode. The plant participates in the spot market and in the secondary regulation reserve market, taking into account the regulation energy due to the real-time use of the regulation-up and -down reserves. The proposed model is used to compare the maximum theoretical income of the plant with and without considering hydraulic short-circuit operation. Numerical results demonstrate that the operation in hydraulic short-circuit mode could help significantly to enlarge the income of the power plant and that the secondary regulation reserve market might be the main source of revenue in a realistic setting characterized by a high level of renewable energy sources in the generation mix. info:eu-repo/semantics/draft

One of the most important key factors for the development of non-urban areas is infrastructure, and energy generation is one of the fundamental infrastructure elements. This paper provides a new solution for energy generation based on wood chips which has a multi-sector effect because the authors offer to combine planning of forest cleaning cutting with bioenergy generation in one complex project, which will have socio-economic and ecological effects. The situation with forest fires makes the authors’ idea more attractive because after forest fires the problem of cleaning cutting in forest becomes very important and urgent by ecological and economical points: after cleaning cutting there are a lot of low quality wood which can be recycled into chips for the production bioenergy by the authors’ idea. This enriched methodology has successfully been applied into the regional strategical planning in the field of bioenergy and forestry of the Ural region of Russia; however, it is suitable for applications in regional development in any non-urban forested region of the world. © 2017, Editura Universitatii din Bucuresti. All rights reserved.

Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Sudden changes in load or generation can be considered as anomaly depending on the implemented state estimation method. Additionally, considering power grid as a cyber physical system, state estimation becomes vulnerable to false data injection attacks. The existing methods for anomaly classification cannot accurately classify (discriminate between) the above mentioned three types of anomalies, especially when it comes to discrimination between sudden load changes and false data injection attacks. This paper presents a new algorithm for detecting anomaly presence, classifying the anomaly type and identifying the origin of the anomaly, i.e., measurements that contain gross errors in case of bad data, or buses associated with loads experiencing a sudden change, or state variables targeted by false data injection attack. The algorithm combines analytical and machine learning (ML) approaches. The first stage exploits an analytical approach to detect anomaly presence by combining χ2-test and anomaly detection index. The second stage utilizes ML for classification of anomaly type and identification of its origin, with particular reference to discrimination between sudden load changes and false data injection attacks. The proposed ML based method is trained to be independent of the network configuration which eliminates retraining of the algorithm after network topology changes. The results obtained by implementing the proposed algorithm on IEEE 14 bus test system demonstratethe accuracy and effectiveness of the proposed algorithm.

AbstractThis paper proposes a Maxwell's equations finite difference time domain (FDTD) approach for electromagnetic transients in ground electrodes in order to take into account the non linear effects due to soil ionization. A time variable soil resistivity method is used in order to simulate the soil breakdown, without the formulation of an initial hypothesis about the geometrical shape of the ionized zone around the electrodes. The model has been validated by comparing the computed results with available data found in technical literature referred to concentrated earths. Some application examples referred to complex grounding systems are reported to show the computational capability of the proposed model. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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