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, H2PCN) 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. In addition, given the interaction energy of the complexes, hydrogen bond complexes of structure II have the highest stability compared to other structures. In all studied complexes, the strength of the interactions depended on the electronegativity of the halogen atoms. The characteristics and nature of the whole three types of complexes were examined and evaluated with natural bond orbital (NBO), atom in molecules (AIM), molecular electrostatic potential (MEP) maps, non-covalent interaction (NCI) index, and electron density difference (EDD) analyses. METHOD: The optimization of all complexes and corresponding monomers was conducted through the ab initio method, employing the MP2 level along with the aug/cc-pVTZ basis set for all atoms, except for the iodine (I) atom, for which the aug-cc-pVTZ (PP) basis set was employed. Subsequent frequency calculations were executed to ascertain the minimum energy state of the complexes at the MP2 level and the aug/cc-pVTZ basis set, utilizing Gaussian09 software. The MEP maps of the monomers were generated using the analysis-surface suite (WFA-SAS) software package. To probe the orbital interactions within the studied complexes, NBO analysis was performed employing NBO software. The assessment of bond nature, topological features, and electron density values at critical points for the studied complexes was undertaken using AIMAll software. The NCI index was derived utilizing Multiwfn software, and its three-dimensional representation was rendered using VMD software.

Competition of hydrogen, tetrel, and halogen bonds in COCl2-HOX (X=F, Cl, Br, I) complexes.

The present study investigates the competition between hydrogen, halogen, and tetrel bonds from the interaction of COCl2 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. Adduct I complexes are more stable than others, and adduct V halogen bonded complexes are more stable than adduct II complexes. These results are in agreement with their NBO and AIM results. The stabilization energy of the XB complexes depends on the nature of both the Lewis acid and base. The stretching frequency of the O-H bond in adducts I, II, III, and IV displayed a redshift, and a blue shift was observed in adduct V. The results for the O-X bond showed a blue shift in adducts I and III and a red shift in adducts II, IV, and V. The nature and characteristics of three types of interactions are investigated via NBO analysis and atoms in molecules (AIM).

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. To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO's software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land-grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO's MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.

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. We modeled the potential impact of climate change on brown bear distribution and habitat connectivity by the years 2050 and 2070 under four representative concentration pathways (RCPs) of two general circulation models (GCMs): BCC-CSM1-1 and MRI-CGCM3. Our projections revealed that the current species' range, which encompasses 6749.8 km2 (40.8%) of the landscape, will decline by 10% (2050: RCP2.6, MRI-CGCM3) to 45% (2070: RCP8.5, BCC-CSM1-1). About 1850 km2 (27.4%) of the current range is covered by a network of conservation (CAs) and no-hunting (NHAs) areas which are predicted to decline by 0.64% (2050: RCP2.6, MRI-CGCM3) to 15.56% (2070: RCP8.5, BCC-CSM1-1) due to climate change. The loss of suitable habitats falling within the network of CAs and NHAs is a conservation challenge for brown bears because it may lead to bears moving outside the CAs and NHAs and result in subsequent increases in the levels of bear-human conflict. Thus, re-evaluation of the network of CAs and NHAs, establishing more protected areas in suitable landscapes, and conserving vital linkages between habitat patches under future climate change scenarios are crucial strategies to conserve and manage endangered populations of the brown bear.

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. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

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. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.

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). An influence diagram was developed to identify the factors influencing wildfire in arid and semi-arid areas of Iran, and it was populated with probabilities to produce a BBNs model. The behavior of the model was tested using scenario and sensitivity analysis. Land cover/use, mean annual rainfall, mean annual temperature, elevation, and livestock density were recognized as the main variables determining wildfire occurrence. The produced model had good accuracy as its ROC area under the curve was 0.986. The model could be applied in both predictive and diagnostic analysis for answering "what if" and "how" questions. The probabilistic relationships within the model can be updated over time using observation and monitoring data. The wildfire BBN model may be updated as new knowledge emerges; hence, it can be used to support the process of adaptive management.

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. Following the recommended procedure, thiourea could be determined with linear calibration graph in 0.03-10.00 microg/ml range. The detection limit of the proposed method is 0.02 microg/ml. Most of foreign species do not interfere with the determination. The high sensitivity and selectivity of the proposed method allowed its successful application to fruit juice and industrial waste water.