In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named “conscious laboratory (CL)”. For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.
Climate change, a global threat of utmost significance, has the potential to trigger shifts in biodiversity distribution and the emergence of novel ecological communities. For species with limited dispersal abilities or geographical barriers within their range, niche conservatism can further constrain their ability to colonize and thrive in future suitable habitats, rendering them more vulnerable to the effects of global climate change. In this study, an ensemble modeling framework and climatic niche dynamics analysis were employed to forecast the impact of climate change on climatic niche dimensions and transferability of two indicator species, namely, Ziziphus spina-christi and Ziziphus nummularia, in Iran. Our analysis revealed that, under optimistic and pessimistic climate change scenarios, the habitat suitability for Z. spina-christi will expand during 2041-2070 and 2071-2100, predominantly towards higher latitudes. In contrast, Z. nummularia is anticipated to experience a general decline in habitat suitability during the same periods and climate scenarios, resulting in the loss of portions of its southern range. Our examination of climatic niche dynamics unveiled a relatively low observed niche overlap between the two species. Randomization tests further underscored the adherence of these species to their historical niches, suggesting challenges in adapting to changing climatic conditions. The integration of predictive models and niche dynamics analysis indicates that these species may encounter difficulties migrating to the tracked niches in distant habitats due to their preserved niches. Given the high sensitivity of arid ecosystems to environmental disturbances and slow recovery rates, the repercussions for arid land biodiversity are indeed profound and irrevocable. Conservation and management measures, including identifying priority areas and creating artificial habitats, are crucial to protect these species’ habitats.The study’s conclusions are valuable for biodiversity conservation authorities, local stakeholders, and individuals dedicated to preserving Ziziphus habitats within the study area.
Abstract Combining genetic data with ecological niche models is an effective approach for exploring climatic and nonclimatic environmental variables affecting spatial patterns of intraspecific genetic variation. Here, we adopted this combined approach to evaluate genetic structure and ecological niche of the Indian gray mongoose ( Urva edwardsii ) in Iran, as the most western part of the species range. Using mtDNA, we confirmed the presence of two highly differentiated clades. Then, we incorporated ensemble of small models (ESMs) using climatic and nonclimatic variables with genetic data to assess whether genetic differentiation among clades was coupled with their ecological niche. Climate niche divergence was also examined based on a principal component analysis on climatic factors only. The relative habitat suitability values predicted by the ESMs for both clades revealed their niche separation. Between‐clade climate only niche comparison revealed that climate space occupied by clades is similar to some extent, but the niches that they utilize differ between the distribution ranges of clades. We found that in the absence of evidence for recent genetic exchanges, distribution models suggest the species occurs in different niches and that there are apparent areas of disconnection across the species range. The estimated divergence time between the two Iranian clades (4.9 Mya) coincides with the uplifting of the Zagros Mountains during the Early Pliocene. The Zagros mountain‐building event seems to have prevented the distribution of U. edwardsii populations between the western and eastern parts of the mountains as a result of vicariance events. Our findings indicated that the two U. edwardsii genetic clades in Iran can be considered as two conservation units and can be utilized to develop habitat‐specific and climate change‐integrated management strategies.
Abstract Background Humans have altered fire regimes across ecosystems due to climate change, land use change, and increasing ignition. Unprecedented shifts in fire regimes affect animals and contribute to habitat displacement, reduced movement, and increased mortality risk. Mitigating these effects require the identification of habitats that are susceptible to wildfires. We designed an analytical framework that incorporates fire risk mapping with species distribution modeling to identify key habitats of Ursus arctos with high probability of fire in Iran. We applied the random forest algorithm for fire risk mapping. We also modeled brown bear habitats and predicted connectivity between them using species distribution models and connectivity analysis, respectively. Finally, the fire risk map, critical habitats, and corridors were overlaid to spatially identify habitats and corridors that are at high risk of fire. Results We identified 17 critical habitats with 5245 km 2 of corridors connecting them, 40.06% and 11.34% of which are covered by conservation areas, respectively. Our analysis showed that 35.65% of key habitats and 23.56% of corridors are at high risk of fire. Conclusions Since bears habitat in this semi-arid landscape rely on forests at higher altitudes, it is likely that shifting fire regimes due to changing climate and land use modifications reduce the extent of habitats in the future. While it is not well known how fire affects bears, identifying its key habitat where wildfires are likely to occur is the first step to manage potential impacts from increasing wildfires on this species.
Previous studies have shown that the presence of nitrate in drinking water can cause several diseases especially in the infants, such as cancer and blue baby. The Environmental Protection Agency (EPA) has since adopted the 50 mg/l standard as the maximum contaminant level (MCL) for nitrate for regulated public water systems. This study aimed to evaluate the concentration of nitrate in the drinking water wells of Birjand, Iran, using inverse distance weighting (IDW) model and also using remote sensing (ENVI software) for studying the vegetation area. In this study, the average annual nitrate level in 2015 was measured from 19 wells around Birjand that were used as rural water supplies. For the zoning of nitrate concentration in the groundwater of Birjand, we used Arc GIS software by using IDW interpolation methods, and for studying the vegetation area and its effect on the groundwater quality we used Landsat Archive image (L4-5 TM sensor) and ENVI 4.7 software. The mean concentration of nitrate was 25.89 ± 12.33 mg/l in the groundwater. Nitrate concentration was more than the standard range (50 mg/l) according to the National Standard of Iran (No. 1053) in one well in the studied zone. Based on the information obtained from remote sensing, agricultural activities were an effective factor in increasing the concentrations of nitrate in the groundwater of the studied area.
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