Ab initio investigation of the competition of pnicogen, halogen, and hydrogen bonds resulting from the interactions between cyanophosphine and hypohalous acids.

Context: The complexes formed as a result of the interactions between cyanophosphine (CP, HPCN) and hypohalous acid molecules (HOX, X = F, Cl, Br, and I) were studied by employing ab initio computations conducted at the MP2/aug-cc-pVTZ level. Three types of complexes were acquired (I, II, and III) as a result of the (O∙∙∙P) pnicogen bond, the (N∙∙∙H) hydrogen bond, and the (N∙∙∙X) halogen bond interaction, respectively. The results of harmonic vibrational frequency calculations with no imaginary frequencies confirmed the structures as minima.

Competition of hydrogen, tetrel, and halogen bonds in COCl-HOX (X=F, Cl, Br, I) complexes.

The present study investigates the competition between hydrogen, halogen, and tetrel bonds from the interaction of COCl with HOX using quantum chemistry simulations at the MP2/aug-cc-pVTZ computational level, in which five configurations were optimized, including adducts I -V. Two hydrogen bonds, two halogen bonds, and two tetrel bonds were obtained for five forms of adducts. The compounds were investigated using spectroscopic, geometry, and energy properties.

Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible.

Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms.

Plant Ecological Unit's (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran.

Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform.

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran.

Modeling climate change impacts on the distribution of an endangered brown bear population in its critical habitat in Iran.

Climate change is one of the major challenges to the current conservation of biodiversity. Here, by using the brown bear, Ursus arctos, in the southernmost limit of its global distribution as a model species, we assessed the impact of climate change on the species distribution in western Iran. The mountainous forests of Iran are inhabited by small and isolated populations of brown bears that are prone to extinction in the near future.

Risk of fire occurrence in arid and semi-arid ecosystems of Iran: an investigation using Bayesian belief networks.

Identifying areas that have a high risk of burning is a main component of fire management planning. Although the available tools can predict the fire risks, these are poor in accommodating uncertainties in their predictions. In this study, we accommodated uncertainty in wildfire prediction using Bayesian belief networks (BBNs).

A kinetic method for the determination of thiourea by its catalytic effect in micellar media.

A highly sensitive, selective and simple kinetic method was developed for the determination of trace levels of thiourea based on its catalytic effect on the oxidation of janus green in phosphoric acid media and presence of Triton X-100 surfactant without any separation and pre-concentration steps. The reaction was monitored spectrophotometrically by tracing the formation of the green-colored oxidized product of janus green at 617 nm within 15 min of mixing the reagents. The effect of some factors on the reaction speed was investigated.