Fungitoxidade in vitro de extratos vegetais sobre Exserohilum turcicum (Pass) Leonard & Suggs In vitro fungitoxicity of plant extracts on Exserohilum turcicum (Pass) Leonard & Suggs

A helmintosporiose, causada pelo fungo Exserohilum turcicum, é uma das principais doenças do milho-pipoca cultivado no Brasil. Devido às características da cultura, como porte da planta, extensão da área de plantio e rentabilidade econômica, o emprego de resistência genética e controle químico têm sido as principais formas de controle da doença. O emprego de agrotóxicos na agricultura tem levado riscos à saúde humana e freqüentes danos ao meio ambiente. Assim, na busca de métodos alternativos para o controle da helmintosporiose foi avaliado o efeito fungitóxico dos extratos vegetais das plantas Achillea milefollium (mil-folhas), Cymbopogon citratus (capim-limão), Artemisia camphorata (cânfora) e Rosmarinus officinalis (alecrim) no crescimento micelial de E. turcicum, em dois meios de cultura (BDA - batata-dextrose-ágar; e LCH - lactose caseína hidrolisada). Os extratos de alecrim e cânfora foram os que apresentaram maior inibição do crescimento micelial nos dois meios de cultura, enquanto que os extratos de mil-folhas e capim limão estimularam o crescimento micelial em meio LCH.Helminthosporiose is caused by the fungus Exserohilum turcicum and represents one of the main diseases in popcorn grown in Brazil. Due to its characteristics, such as plant size, planting area extension and economic profitability, the use of genetic resistance and chemical control has constituted the main procedure against such disease. The use of pesticides in agriculture has resulted in risks to the human health and frequent damages to the environment. Thus, the fungitoxic effect of plant extracts of Achillea millefolium (yarrow), Cymbopogon citratus (lemon grass), Artemisia camphorata (camphor) and Rosmarinus officinalis (rosemary) on the mycelial growth of E. turcicum was evaluated by using two culture media (PDA - potato dextrose agar, and LCH - lactose-casein hydrolysate) in order to set alternative methods for controlling helminthosporiose. Rosemary and camphor extracts led to higher mycelial growth inhibition in both culture media, whereas yarrow and lemon grass extracts stimulated mycelial growth in LCH medium

Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

© 2022, The Author(s), under exclusive licence to Springer Nature Limited.Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.N

Overview of JET results for optimising ITER operation

Abstract The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.</jats:p

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures