Eko-socijalni i gospodarski profil herbicida glifozata

By overview of scientific, political, and economic arguments, the structure and profile of glyphosate, the most important agrochemical compound in the world, has been portrayed. Toxicological, ecological and market objections of the opponents of glyphosate are in opposition with the financial and political support to that herbicide which, according to its advocates, enables the protection of crops, higher yields, and postpones the problem of world hunger. The conflict surrounding glyphosate surpasses the narrow scientific and social frames and is a good example of the need to think through and of decision making which involves connection and the totality of reality.Pregledom znanstvenih, političkih i gospodarskih argumenata prikazana je struktura i profil glifozata, najvažnije agrokemikalije na svijetu. Toksikološki, ekološki i tržišni prigovori protivnika glifozata u oprjeci su s financijskom i političkom podrškom tom herbicidu, koji, prema zagovarateljima, omogućuje zaštitu usjeva, veće prinose i odgađa problem gladi u svijetu. Sukob oko glifozata nadilazi uske znanstvene i društvene okvire, te je dobar primjer potrebe promišljanja i odluka koje uključuju povezanost i cjelokupnost stvarnosti

Status of Transgenic Crops in the Encyclical „Laudato si’“

Transgenic crops are introduced by Pope Francis in the encyclical „Laudato si’”. It is the first document originating from Catholic teaching in which genetically modified (GM) crops are explicitly considered. Biodiversity is the main framework in which genetically engineered plants are contemplated. In addition to ecological concerns, the three different aspects have been presented: socioeconomic, scientific, and moral/theological. The interdisciplinary and multiperspective approach has been advocated in order to tackle the controversy of GM crops

Mechanism of 1,2-Hydride Shift in Some Carbocations Involved in Steroid Biosynthesis

The mechanism of 1,2-hydride shift in protosteryl C(20) cation (1A) and in dammarenyl C(20) cation (2A) was investigated by the semi-empirical AM1 method and ab initio quantum Chemical calculations (HF/3-21G level). Stationary points 1A/1B and 2A/2B, and the corresponding transition hydrido-bridged structures 1TS and 2TS were located on the energy surface. Process 1A→1B turned out to be energetically more favorable than process 2A→2B by ca. 9 kcal mol-1, mostly due to the unfavorable steric repulsive interaction between the methyl group at C(14) and the β-oriented side chain at C(17) in 1A and the lack of CC-hyperconjugative stabilization in 1A. The exothermicity of processes 1A→1B and 2A→2B was increased by subsequent introduction of substituents (H, Me, i-Pr, and t-Bu) at C(14). The more pronounced trend in 1A→1B proves that the origin of the relative stability of 1B comes from the steric interactions in 1A. Introduction of the halogen atom (F, Cl, and Br), due to its -I effect and relatively small size, changed the direction of the equilibrium 1AY/1BY, and 1AY was found to be by ca. 3 kcal mol-1 more stable than 1BY

Strojno učenje u fizici čvrstog stanja i statističkoj fizici

In this work, we study an alternative approach to simulating molecular dynamics of large systems over long time periods; the one using machine learning instead of DFT. Studied system is a ruthenium surface which is interacting with hydrogen atoms. We use neural networks to obtain the regression model for the studied system, and train several different architectures of neural networks in order to find the optimal one. For input we use Gaussian descriptors which take Cartesian coordinates of the atoms and translate them in a more suitable form. In this case, there are 20 descriptors for each type of atoms, meaning that input layer of neural network has in total 40 nodes. Optimal architecture was found to be the one with three hidden layers with 50, 30, and 10 nodes, respectively. It was shown how our regression model behaves depending on number of training steps, importance of used descriptors was analyzed, and it was shown how model behaves if Zernike descriptors are used instead of Gaussian, or if cutoff radius is altered.U ovom radu proučavamo alternativni pristup simuliranju molekularne dinamike velikih sustava preko dugih vremenskih perioda; korištenje strojnog učenja umjesto DFT-a. Proučavani sustav sastoji se od površine rutenija koja međudjeluje s atomima vodika. Za konstruiranje regresijskog modela koristimo neuronske mreže te treniramo nekoliko različitih arhitektura mreža kako bismo našli optimalnu. Kao ulaz koristimo Gaussove deskriptore koji kartezijeve koordinate atoma pretvore u oblik pogodniji za opis sustava. U ovom slučaju, postoji 20 deskriptora za svaku vrstu atoma, što znači da u ulaznom sloju neuronske mreže imamo 40 čvorova. Pokazano je da optimalna arhitektura neuronske mreže sadrži tri skrivena sloja, od kojih prvi ima 50 čvorova, drugi 30, a treći 10. Osim toga, pokazano je kako se taj regresijski model ponaša ovisno o broju koraka prilikom treniranja, analizirana je važnost korištenih deskriptora te je proučeno ponašanje modela u slučaju korištenja Zernike deskriptora umjesto Gaussovih, ili mijenjanja polumjera obuhvaćanja atoma