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Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies. [ABSTRACT FROM AUTHOR]

A total of 1,348 endophytic fungal strains were isolated from Ferula ovina, F. galbaniflua, and F. persica. They included Eurotiales (16 species), Pleosporales (11 species), Botryosphaeriales (1 species), Cladosporiales (2 species), Helotiales (6 species), Hypocreales (31 species), Sordariales (7 species), Glomerellales (2 species), and Polyporales (1 species). F. ovina had the richest species composition of endophytic fungi, and the endophytic fungi were most abundant in their roots compared to shoots. Chao, Margalef, Shannon, Simpson, Berger--Parker, Menhinick, and Camargo indices showed that F. ovina roots had the most endophytic fungal species. The frequency distribution of fungal species isolated from Ferula spp. fell into the log-series model, and F. ovina roots had the highest Fisher alpha. The dominance indices showed that there are no dominant species in the endophytic fungal community isolated from Ferula spp., indicating community stability. Evenness values were 0.69, 0.90, 0.94, and 0.57 for endophytic fungi isolated from F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots, respectively, indicating a species distribution that tends toward evenness. The fungal species community isolated from each of F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots was a diverse species group originating from a homogeneous habitat. Their distribution followed a log-normal distribution, suggesting that the interactions of numerous independent environmental factors multiplicatively control species abundances. Principal component analysis showed that the highest species diversity and dominance were observed in the endophytic fungal community isolated from F. ovina and F. persica roots, respectively. [ABSTRACT FROM AUTHOR]

This electron microscope study investigated the micromorphological characteristics of the leaf epidermis of 5 species (10 populations) of Ribes (Grossulariaceae) in Iran: R. biebersteinii, R. uva‐crispa, R. orientale, R. aureum, and R. khorasanicum. Samples were collected and identified, and the similarities between the species were evaluated using information from micromorphological traits of leaf epidermis and multivariate analysis (cluster analysis and principal component analysis). The traits useful for discriminating the species were presence or absence of hairs and glandular trichomes, presence or absence of granular with verrucae on hair surface, position of hairs on epidermal surface, hair location on the leaf (lamina, leaf margin, midrib), stomata shape, wax distribution on stomata, prominence and shape of anticlinal walls and outer periclinal layer, and epicuticular wax sculpturing. An identification key based on these leaf micromorphological characters is provided for Ribes species in Iran. The multivariate analysis of micromorphological traits was efficient for inter‐ and intra‐specific delimitation. The present findings correspond to a large extent with existing classifications. [ABSTRACT FROM AUTHOR]

Employing robust methodologies, including principal component analysis, autoregressive moving average, Fourier bootstrap dynamic autoregressive distributed lag, error correction model, and the Breitung–Candelon spectral Granger causality test, this study scrutinizes the impact of export diversification (EXD) on Iran's ecological footprint (EF) from 1997 to 2020, considering economic sanctions (ESI), trade openness (TOP), energy consumption per capita (ECpc), globalization (KOF), and real GDP per capita (RGDPpc). Findings consistently affirm a positive environmental impact of EXD, revealing a nuanced temporal pattern. Notably, the short-term impact (− 0.645) is more pronounced than its long-term counterpart (−.020). Increased industrial activities due to globalization (10% rise) lead to 4.26% and 1.64% EF degradation in the long and short term. Conversely, due to Iran's heavy reliance on fossil fuels, a 10% rise in ECpc correlates with 1.63% and 3.81% long- and short-term environmental quality reduction. ESI demonstrates a dual impact, improving short-term environmental quality but contributing to long-term degradation. Frequency-domain causality analysis highlights EXD and KOF as short- and long-term causes of EF, ESI, and TOP as medium- to long-term causes and RGDPpc as a long-term cause. These findings emphasize the need for sustainable policies, stringent environmental standards, and a balanced approach to fostering economic growth while preserving the environment. [ABSTRACT FROM AUTHOR]

Background: Medicinal plants, such as Ajuga chamaecistus Ging. ex Benth. are a natural and available source of treatment for a wide range of diseases. The objective of the present study was to assess the morphological and biochemical variation of 70 accessions of this species collected from seven geographical areas of Markazi province in the center of Iran. Results: The measured traits exhibited considerable variability across the populations. Positive correlations were observed between antioxidant activity and total phenolic content, as well as total flavonoid content. Principal component analysis showed six components explaining 72.15% of the total variance, and the PC1 explained 20.68% of the total variance. The Ward dendrogram based on morphological variables identified two main clusters. Morphological analysis of A. chamaecistus showed a high variation between qualitative and quantitative traits that help the breeders for selecting the desired genotypes. The accessions collected from the Robat-Mil area showed the highest values for the recorded morphological characteristics. Also, the populations of Robat-Mil, Hassanabad, and Khaneh-Miran were characterized by high values of total phenolic content, total flavonoid content, and antioxidant activity, which can be used in various industries, including pharmaceuticals, cosmetics, and food. Conclusions: Overall, the present results showed that the best place for the growth of A. chamaecistus with the production of significant contents of phenol and flavonoid is in Robat-Mil area. [ABSTRACT FROM AUTHOR]

Basil (Ocimum basilicum L.) is an aromatic plant used in foods, pharmaceuticals, and in cosmetics. In this study, the morphology and yield of 17 different basil genotypes and three cultivars (Moonlight, Midnight, and Dino) from Türkiye were analyzed in 2019 and 2020. A wide range of variations in plant height, number of branches, and weights of fresh and dry herbs weights were observed. The genotypes PI 531,396 from Hungary, PI 174,284 from Türkiye, PI 253,157 from Iran, and the Dino cultivar had the highest plant yield compared with the other genotypes and cultivars. The leaf color parameters (L*, a*, b* C*, H° and WI) were also determined for the plants. The highest lightness (L*) values were recorded in the PI 190,100 genotype, and the highest yellowness/blueness (b*) and hue angle (H°) values were recorded in the PI 207,498 genotype. The highest redness/greenness (a*), chroma (C*), and whiteness index (WI) values were recorded in the PI 197,442, PI 190,100, and PI 531,396 genotypes, respectively. Except for the L* values, the leaf color parameters clustered on the same side of the principal component analysis (PCA) axis. Dendrogram analysis divided the genotypes and cultivars into two main groups, and the main A group contained the cultivar Dino only. Therefore, the study identified important genotypic characteristics of the examined basil genotypes and cultivars that could be utilized to develop new cultivars. [ABSTRACT FROM AUTHOR]

Iran is located along the Central Asian corridor, a natural artery that has served as a cross‐continental route since the first anatomically modern human populations migrated out of Africa. We compiled and reanalyzed the HVS‐I (hypervariable segment‐I) of 3840 mitochondrial DNA (mtDNA) sequences from 19 Iranian populations and from 26 groups from adjacent countries to give a comprehensive review of the maternal genetic variation and investigate the impact of historical events and cultural factors on the maternal genetic structure of modern Iranians. We conclude that Iranians have a high level of genetic diversity. Thirty‐six haplogroups were observed in Iran's populations, and most of them belong to widespread West‐Eurasian haplogroups, such as H, HV, J, N, T, and U. In contrast, the predominant haplogroups observed in most of the adjacent countries studied here are H, M, D, R, U, and C haplogroups. Using principal component analysis, clustering, and genetic distance‐based calculations, we estimated moderate genetic relationships between Iranian and other Eurasian groups. Further, analyses of molecular variance and comparing geographic and genetic structures indicate that mtDNA HVS‐I sequence diversity does not exhibit any sharp geographic structure in the country. Barring a few from some culturally distinct and naturally separated minorities, most Iranian populations have a homogenous maternal genetic structure. [ABSTRACT FROM AUTHOR]

Questions: Plant communities have been shown to include several functional groups of species that may have contrasting responses to the effects of dominant neighbours, although potentially balancing at the community level. We aimed to assess the potential of species‐group vs community‐level responses of subordinate species to explain variation in the effects of the dominant shrub Artemisia sieberi on understorey species along a grazing intensity gradient in arid steppes of northeast Iran. We also aimed to assess whether species‐group responses help explain variation in community composition. Locations: An Artemisia steppe community in Golestan National Park (northeast Iran). Methods: We used the relative interaction index (RII) to quantify the effects of a shrub on the cover of the 12 most frequent subordinate species. We conducted a first principal component analysis (PCA) on species RII followed by cluster analysis to group species depending on their responses to the shrub, and a second PCA on subordinate species composition. Results: At the community level, subordinate species showed strong competition at the low grazing level, weak facilitation at the intermediate level and weak competition at the high grazing level; a unimodal pattern with a switch back to competition in extremely disturbed conditions that is inconsistent with ecological theories. However, species‐group analyses showed contrasting subordinate species responses, supporting either the decrease in competition with increasing disturbance scenario or the collapse of facilitation, but never a switch back to competition as predicted by recent facilitation theory along resource gradients. Moreover, these contrasting species‐group responses significantly explained community composition. Conclusions: Our study provides new evidence, along a grazing intensity gradient, that communities include different species groups with contrasting responses to dominant species. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran's Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions. [ABSTRACT FROM AUTHOR]

Context. Maize (Zea mays L.) is one of the most economically important plants of the cereal family; it has value as human food, livestock feed, and as a component of industrial products. Aims. This study focused on genetic diversity and existence of genetic divergence among promising maize inbred lines in Iran. Methods. A commercial maize 600K SNP (single-nucleotide polymorphism) array was used to inspect genetic variability among 93 maize inbred lines. Key results. The rate of transition mutation was twice as high as transversion mutation, and the density of detected SNPs was greater close to telomere regions of maize chromosomes. Considering the fluctuation of observed, expected and total heterozygosity and fixation index values across maize chromosomes, as well as polymorphism information content values, there is a high level of genetic variability among the studied maize panel. In addition, discriminant analysis of the principal components revealed four subpopulations in which the subpopulation 'Line' was distinct from other subpopulations and had no genomic overlap with them. Selection signature analysis revealed 177 regions harbouring 75 genes that differentiate among subgroups. Detected genes had a role in the mitogen-activated protein kinase signalling pathway, spliceosome, protein processing in endoplasmic reticulum, and hormone signal transduction. Conclusions. We conclude that remarkable genetic diversity and differentiation exists among the studied maize subpopulations. The most differentiated SNPs among the subpopulations were associated with important biological processing genes and pathways. Implications. The findings provide valuable insights for future maize breeding programs through exploitation of heterosis, as well as marker-assisted selection. [ABSTRACT FROM AUTHOR]

The tolerance of aquatic organisms to stressors is widely used to monitor and evaluate the condition of freshwater ecosystems. Tolerance values (TV) derived from analyses of the relationship between species and their environment are considered to be more objective than those that rely on expert opinion. We used principal component analysis (PCA) to derive a generalized stressor gradient based on physicochemical characteristics and physical habitat quality and structure. Scores of the first principal component axis (PC1) were used to estimate TV for 37 fish species collected from 54 sites in the Karun River basin, Iran. PCA of 17 variables identified stressors that were influential such as total phosphate, total nitrogen, total coliform, and habitat and morphological score. The species were separated into three categories on the stressor gradient: sensitive (18.9%), semi‐tolerant (48.6%), and tolerant species (32.4%). Based on these results we developed the Karun Fish Tolerance Index (KFTI) and demonstrated that it performed well in separating the least, moderate, and most disturbed sites in the study area. The discrimination efficiency of the KFTI was 82.5%, which makes it a robust management tool for the protection and conservation of streams and rivers in the Karun River watershed. TV developed here reflect objective characteristics of the sensitivity of fish species to the predominant stressors in the Karun and similar systems. [ABSTRACT FROM AUTHOR]

Aims: A longer life and the growing population of older adults are the achievements of the 20th century. The World Health Organization considers ageism an important barrier to age‐appropriate care for older adults. The aim of this study was to translate and validate the ageism scale for dental students in Iran (ASDS‐Persian). Methods: The 27‐question ASDS was translated from English into Persian (Farsi) and completed by 275 dental students from two universities in Isfahan, Iran. Principal component analysis (PCA), internal consistency reliability, and discriminant validity were estimated. Furthermore, in this study we conducted an analytical cross sectional study among dental students of Isfahan province (two universities) with the aim of proving data regarding their ageism beliefs and attitudes. Results: PCA revealed an 18‐question, four‐component scale with acceptable validity and reliability. The four components are 'barriers/concerns on dental treatment in older adults', 'opinions about older adults', 'practitioner's perspective,' and 'older adults' perspective'. Conclusion: This preliminary validation of ASDS‐Persian produced a new 18‐question scale with four components with acceptable validity and reliability. This instrument could be further tested in larger samples in Farsi speaking countries. [ABSTRACT FROM AUTHOR]

Drought is a process that is considered as a part of arid and semi-arid climatic regions' characteristics. In recent years, droughts have occurred with more severity and frequency in most regions of the world (Spinoni et al. in Int J Climatol 34(8):2792-2804, 2014). Also, drought monitoring systems can be considered as appropriate tools for drought crisis and risk management in the studied regions. In the present study, the drought condition in the Zarrine Rud basin in Iran was studied by the aggregated drought index (ADI) and the soil and water assessment tool (SWAT) model for the period of 21 years (1987–2008). In the first step, due to the unavailability of observational data on soil moisture and potential evapotranspiration variables, the SWAT model was developed based on the model presented by Ahmadzadeh (2012). Furthermore, the ADI values for each month were evaluated based on the principal components analysis (PCA) method, namely using monthly data on rainfall, soil moisture, potential evapotranspiration, reservoir storage volume, and river flow. Then, the time series of ADI values were employed for drought monitoring in this basin and compared to the standardized precipitation index (SPI) for the historical drought of 1998 to 2001. The results confirmed that the ADI was more transparent and appropriate to describe the severity and duration of the drought. Also, it could be recommended to monitor the drought in this basin. [ABSTRACT FROM AUTHOR]

Delineation of multivariate management zones is an effective approach to implement sustainable agriculture. Super-high density (SHD) is an effective growth strategy that is widely used in all olive-producing countries, particularly in Iran. The present field-scale study was carried out in the 120 hectare SHD planting system of 'Arbequina' olive orchards to provide a full picture of the management zones model automated in the GIS model builder. Geostatistical techniques were used to characterize the spatial variability patterns and distribution maps of three different groups of soil physical and chemical parameters and leaf nutrient contents. Principal component analysis (PCA) and weighted overlay analysis revealed a homogeneous management class for the above three groups. Finally, irrigation and nutrient management zones were determined by a fuzzy K-means (FKM) algorithm. Results showed that soil and leaf properties had a moderate to strong degree of spatial correlation; the spherical model, with a lower mean RSS and a higher R2, offered a good fit; also, Co-Kriging (CoK) produced better estimates than other methods. The variables showing the highest variances in the PCA were used as inputs for the FKM algorithm. The optimum number of irrigation and nutrient management zones was 4 and 5, respectively. The study area, in terms of percentage, was divided into MZ3 > MZ2 > MZ4 > MZ1 for irrigation management and MZ2 > MZ3 > MZ1 > MZ4 > MZ5 for the nutrient management zones. GIS model builder acted successfully under a large number of input layers. These findings, thus, suggest developing this workflow of the GIS model builder by the desired modifications in similar studies on the regional scale for other orchards or crops. [ABSTRACT FROM AUTHOR]

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Background: Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars. Results: Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g− 1.day− 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants. Conclusions: Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes. [ABSTRACT FROM AUTHOR]

Background: Chronic diseases and metabolic disorders are prevalent health concerns that often escalate with increasing age and thus affect older individuals. The proportion of the elderly population in Iran increased from 7.22% in 2006 to 12.0% in 2023. The current study aimed to evaluate lifestyle patterns and lifestyle risk factors among patients with metabolic syndrome (MetS) based on dietary, physical activity, and smoking, as well as MetS components. Methods: This cross-sectional study included 582 older people with MetS living in Yazd, Iran. Latent class analysis (LCA) was used to determine the lifestyle behaviors of diet patterns, smoking, and physical activity. Dietary intake was measured using a validated food frequency questionnaire, and dietary patterns were identified using principal component analysis (PCA). Clinical measurements of MetS components were examined using relevant guidelines. Results: The mean age of the participants was 72.71 years (SD = 5.57). Using PCA, two dietary patterns were identified: traditional patterns (e.g., fruits, fish, poultry, vegetables, meats, salt, and sugar sweetened beverages) and high-fat patterns (e.g., high-fat dairy). Applying LCA identified two classes of lifestyle patterns. About 35% (n = 204) of the participants were categorized in a low-risk class (I) and characterized by physical activity (0.93%, n = 190), a traditional pattern for diet (61%, n = 122), and zero probability of smoking. About 65% (n = 378) of the patients were categorized in high-risk class (II) and characterized by low physical activity levels (69%, n = 261), cigarette smoking (71.6%, n = 271), and a high-fat dietary pattern (56.9%, n = 215). Conclusion: The results of our study indicated two distinct classes within the patients. In class I, aging patients with MetS exhibited characteristics such as engagement in physical activity and having a traditional pattern for diet. Class II, with a higher prevalence of lifestyle risk factors, included individuals who engaged in cigarette smoking, displayed low physical activity (69%), and having a high-fat diet. The combination of these lifestyle factors exposed them to a heightened risk of developing MetS. The findings could guide healthcare professionals to be aware of the associations between different lifestyle risk factors and to focus on multiple behaviors at the same time. [ABSTRACT FROM AUTHOR]

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA. [ABSTRACT FROM AUTHOR]

Fossil-fuel consumption in land freight is over 19%. Alternative fuel trucks (AFTs) help decrease greenhouse gas emissions. However, AFT preferences in international land transit have received little attention due to passing through various countries with different financial and regulation plans. This variety affects AFTs' market share. This study analyzes factors influencing AFT preferences in international land transit. A questionnaire (designed in four sections) was distributed among international transport companies in Iran and Europe. A principal component analysis helped extract principal components composed of cognitive, environmental, policy, practical, and economic variables. The multinomial logit models include 26 meaningful variables. The marginal effect analysis shows that the service quality of the manufacturer, importance of greenhouse gas mitigation, and social responsibility do not significantly elevate AFT preferences. In addition, cargo type influences AFT choice. The results of this study help to identify the barriers affecting AFTs' market share, which can positively impact air pollution. [ABSTRACT FROM AUTHOR]

Prunus scoparia (Spach) C. K. Schneid is among the most prevalent species which has the potential of being used as a dwarf rootstock for the cultivated almond. In the present study, the phenotypic variation of 521 wild accessions of this species naturally grown in 29 areas of 11 provinces in Iran was assessed. The accessions investigated showed significant differences based on the measured traits. The majority of the characters measured (90 out of 100) exhibited a coefficient of variation of higher than 20.00%, indicating considerable variation among the accessions. The range of nut-related characters was as follows: nut length: 9.72–22.87 mm, nut width: 5.81–15.54 mm, nut thickness: 5.67–12 mm, and nut weight: 0.18–0.99 mm. The range of kernel-related characters was as follows: kernel length: 6.83–19.23 mm, kernel width: 4.28–10.32 mm, kernel thickness: 2.16–7.52 mm, and kernel weight: 0.03–0.37 g. Kernel weight exhibited positive and significant correlations with nut length (r = 0.57), nut width (r = 0.54), nut thickness (r = 0.42), nut weight (r = 0.69), kernel length (r = 0.75), kernel width (r = 0.78), and kernel thickness (r = 0.58). Cluster analysis based on Ward's method showed two different major clusters among all the accessions. Based on the bi-plot created using principal component analysis of population analysis, the studied 29 natural habitats formed four groups. The studied accessions showed considerable variation in terms of the measured traits within and among populations. This variation is due to cross-pollination, cross-incompatibility, natural hybridization, propagation by seeds, gene flow, and exchange of plant material between the study areas. By using crosses between accessions of different regions, it is possible to increase the amount of variability in different traits of wild almonds. [ABSTRACT FROM AUTHOR]

In this study, we analyzed nuclear DNA (nDNA) variation by means of genotyping-by-sequencing (GBS) of three disc-bearing and seven non-disc-bearing Garra tashanensis from the Tashan Cave in Iran to clarify their systematic and ecological relationships. We performed genetic differentiation, NeighborNet, Admixture, and principal component analyses on genomic data. Admixture and principal component analyses revealed clear genetic differentiation between disc-bearing and non-disc-bearing morphotypes, which was not detected by mtDNA markers in previous work. However, there was a non-disc-bearing individual that clustered with disc-bearing individuals. Based on the results of this study, the most parsimonious justification for coexistence of disc-bearing and non-disc-bearing forms of Garra in Tashan Cave is a combination of mechanisms similar to character release (intra-specific morphological diversification to exploit a wider range of habitats in the absence of competitors or predators) and relaxed selection (no more selective advantage for a previously advantageous trait as a result of ecological changes). The detected genetic structure may be an indication of reproductive isolation between the two groups. These data imply that the respective forms of the Tashan Cave barb could be a case of ongoing sympatric speciation, but also that disc form cannot be used as a taxonomic character for Tashan Cave barb forms. [ABSTRACT FROM AUTHOR]

This study aimed at investigating pollution sources and indicators in three study sites, namely, an industrial town (It), landfill (Lf), and wastewater treatment plant (WTP) in the vicinity of Ilam city, west of Iran. To achieve this objective, the sampling was performed from the topsoil (0–10 cm) of three sites (It, Lf, and WTP) with pollution potential. Some soil physico-chemical properties and total concentration of HMs including Cd, Pb, Ni, Cr, and Co, pollution indicators and sources of pollution were evaluated by statistical analysis. The results of the geoaccumulation index (Igeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI) showed that the studied areas were contaminated with HMs. The highest average of Cd and Pb were 1.45 and 440.19 mg kg–1, respectively, in the Lf. Whereas, the highest average of Ni and Cr were 106.53 and 43.50 mg kg–1, respectively, in the IT, while the highest average of Co was 81.69 mg kg–1 in the WTP. According to the results of the principal components analysis (PCA) and clustering, a small part of the Pb, Ni, and Co amounts in the soils had the natural origin, while the results confirmed that a significant proportion of Pb, Cd, and Cr concentrations associated with human-caused hazards. Moreover, all areas (especially Lf and WTP sites) were highly contaminated with Cd. Due to the higher mobility of Cd and its human health hazards, this metal should be given more attention in the study sites of Ilam city. In general, based on our results, more paying attention needs to be considered by decision-makers and stakeholders to manage and control pollution production sources in the soils of the study sites. [ABSTRACT FROM AUTHOR]

Climate classification provides a framework for a better understanding of the dominant weather patterns in different regions of the Earth. This study aims at identifying climate zones in Iran based on the analysis of monthly temperature and precipitation over 139 synoptic stations across Iran during the period 1991–2020. Based on the application of the principal component analysis, we identified six distinct climate zones in Iran: mild and humid, cool and sub‐humid, cold and temperate semi‐arid, warm and semi‐arid, cool and arid, and warm and hyperarid. The highest precipitation occurs in the southern coastal plains of the Caspian Sea, characterized by a mild and humid climate. The climate of western Iran is identified as cool and sub‐humid, while northwestern Iran is characterized by a cold and temperate semi‐arid climate. Southwestern Iran is identified as a region with a warm and semi‐arid climate, while northeastern Iran has a cool and arid climate. Southeastern and central Iran are both characterized by a warm and hyperarid climate. The highest monthly and seasonal precipitation values over Iran occur in March (48.6 mm) and winter (134.2 mm), respectively, while the highest monthly and seasonal mean temperature values occur in July (29.1°C) and summer (28.0°C), respectively. In terms of seasonal variation, the maximum precipitation occurs in the southern coastal plains of the Caspian Sea in autumn, while the minimum occurs in southwestern Iran in summer. Our results have important implications for better understanding and analysing the climatic characteristics across Iran. [ABSTRACT FROM AUTHOR]

It is necessary to select appropriate rainfall series as input to the hydrologic model to access more accurate hydrologic predictions and estimate reliable parameters in the modeling process. For achieving this aim, in the present study, the rainfall multipliers with a combination of Discrete Wavelet Transform (DWT) and principal component analysis (PCA) are applied to select effective rainfall series for modeling. DREAM(ZS) algorithm based on the Markov chain Monte Carlo (MCMC) scheme is used to estimate posterior parameters and investigate prediction uncertainties of a five-parameter hydrologic model, HYMOD. The model's results are then compared to those obtained from the other methods that use only the rainfall multipliers or the raw rainfall data. This study reveals the advantages of using a combined application of DWT and PCA methods to estimate hydrological prediction uncertainty and model parameters accurately. Considering the occasional flash flood incident that occurred in the study region (Tamar basin, which is situated in the Gorganroud river basin, Golestan province, Iran), the results of this research can be useful for forecasting floods accurately and planning for flood control management. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the applicability of UV-Vis and FT-IR fingerprints combined with multivariate statistical tools to classify and authenticate Iranian standard herbs and spices, and their mislabeled and adulterated samples in single and fusion models. The proposed strategy is an alternative, rapid, easy, and economical approach for herbs and spices authentication. Sixty-three samples of different herbs and spices were collected across several cities in Iran. The potency of Savitzky-Golay(SG) smoothing in combination with autoscaling for improving the accuracy of clustering well studied and principal component analysis (PCA), PCA-linear discriminant analysis (PCA-LDA) and partial least squares-discriminant analysis (PLS-DA) were applied for classification. Additionally, data mining of spectral sets was performed using Kohonen self-organization maps (SOMs) of smoothed and unsmoothed individual data sets and classification results were compared. Also, the discriminant models using fusion matrix were built by concatenation of SG smoothed-autoscaled SOMs clusters of FTIR and UV-Vis (SG-autoscaled-SOMs) spectra. The results of different models showed that the accuracy of single SG-autoscaled-SOMs-FTIR data was better than SG-autoscaled-UV-Vis data and the accuracy of SG-autoscaled-SOMs-fusion technique was better than the other models. This method predicted the class of samples more accurately (more than 95%). The authentication and quality of fraud samples were identified more correctly with respect to raw data. [ABSTRACT FROM AUTHOR]

The present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), principal component analysis (PCA), and spatial distribution patterns, to perform element distribution evaluation within the area. The distributions of heavy metals in the entire area were exhibited in the form of maps. The average concentrations of Cd, Cu, Mn, Fe, Zn, and Pb were found to be 0.39, 0.26, 5.3, 4.1, 51.9, and 40.9 mg.kg−1, respectively. Based on the PCA and HCA findings, the heavy metals were divided into two groups. The first group included Pb, Cd, Zn, and Cu. In the first group, altered threshold‐surpassing anthropogenic and lithogenic pollution was found to be the main factor accounting for Pb and Zn. The second group involved Fe and Mn, which could be impacted by either anthropogenic and lithogenic factors. Furthermore, the geo‐statistical results demonstrated higher contents of the heavy metals in the south of the mine and in the vicinity of the mine tailings. It may be concluded from the results that the heavy metal contents of the area are impacted by anthropogenic and lithogenic factors. [ABSTRACT FROM AUTHOR]

Brownfields particularly in old city centers reveal the story of abandonment and concealment, shaping the identity and collective memory of urban areas. Therefore, research and practice must prioritize both reutilization and heritage values. This study centers on the regeneration of historical brownfields in Tehran, the capital of Iran, and assesses public perceptions of redeveloped historical brownfields. Based on their approach to patrimony, the study categorized reclaimed brownfields as interpretive, cultural, or ecological sites. A questionnaire was administered to citizens who visited three sampled sites (n = 385) to collect data. According to the results of principal component analysis (PCA), women preferred the non-economic component, which includes environmental, social, heritage, and aesthetic dimensions, while men and older, highly educated respondents preferred the economic dimension in relation to brownfields. Despite positive attitudes towards brownfield regeneration, environmental and heritage dimensions, especially intangible heritage, are less well-known. However, heritage justifies and determines brownfield redevelopment. Increasing commitment to preserving heritage during brownfield regeneration has a positive effect on the perception of respondents. [ABSTRACT FROM AUTHOR]

Mulberry (Morus spp.) is an economically important plant, and Iran is among the most important mulberry producer countries. Meager information is available on the number of mulberry species and the characteristics of genotypes in Kerman Province. In this study, we used Principal Component Analysis (PCA) to categorize a number of important mulberry traits from different parts of Kerman. A total of fifteen white and black mulberry genotypes were selected from four regions located in different parts of Kerman Province, in 2021. Fourteen qualitative and 15 quantitative variables were measured. The highest and the lowest Coefficient of Variance (CV) was found for leaf shape and fruit length, respectively. The strongest positive correlation was obtained between the number of second-degree shoots and number of shoots. PCA showed that the first five and six components among qualitative and quantitative variables explained 87.82 and 90.87% of the total variation, respectively. The dendrogram biplot classified genotypes into five groups based on quantitative and into four distinct groups in accordance to qualitative variables. The grouping of genotypes did not follow their geographical origin. This study revealed high morphological diversity in the mulberry genotypes dispersed in Kerman, supporting their potential uses for mulberry breeding programs. [ABSTRACT FROM AUTHOR]

Participation in collective actions refers to an individual's behavioral, mental, and emotional engagement in group-situations that motivates him or her to achieve group goals, including environmental protection. The present study aimed at analyzing the intention of members of environmental NGOs to participate in collective proenvironmental activities. To do this, the psychological Dual-pathway Model of Collective Action (DMCA) was used. The research method was descriptive-correlational and was done using survey technique. The statistical population included members of the proenvironmental NGOs in Tehran Province, Iran (N= 680). Out of the population, 248 cases were selected as a sample using stratified random sampling method with proportional assignment. The research instrument was a researcher-made questionnaire and its validity was verified using a panel of case experts and AVE index. Besides, the reliability was confirmed using Cronbach's alpha coefficients, principal component analysis, and composite reliability indices. According to the DMCA, the effects of Perceived Behavioral Control variables about Collective Pro-Environmental Activities (PBCCPEA), Attitude towards Participation in Collective Pro-Environmental Activities (APCPEA), Subjective Norms about Participation in Collective Pro-Environmental Activities (SNPCPEA), Social Identity about Participation in Collective Pro-Environmental Activities (SIPCPEA) were tested on Intention towards Participation in Collective Pro-Environmental Activities (IPCPEA). The results show that this model is able to explain 66% of the variance of IPCPEA changes. The results of this study indicate the need for special attention from the perspective of collective action to make significant changes in the creation of IPCPEA. [ABSTRACT FROM AUTHOR]

Fire blight disease is a major threat to the apple orchards in various regions of the world. The multivariate compositional nutrient diagnosis (CND) was considered to evaluate the effect of plant nutrient imbalance in development of fire blight in 'golden delicious' apple orchards in Fars province, Iran. To establish CND norms and the fire blight severity index, 81 commercial orchards were used. The CND reference norms (V*x) for a low fire blight severity subpopulation were calculated as V*N = 2.813 ± 0.186, V*P = 0.297 ± 0.147, V*K = 2.660 ± 0.149, V*Ca = 2.442 ± 0.218, V*Mg = 1.188 ± 0.061, V*S = 0.595 ± 0.399, V*Fe = −0.952 ± 0.212, V*Mn = −3.132 ± 0.195, V*Zn = −4.040 ± 0.366, V*Cu = −5.032 ± 0.177, V*B = −3.361 ± 0.120, V*Rd = 6.521 ± 0.128. According to the calculated leaf nutrient indices based on the CND reference norms and principal component analysis (PCA), leaf Ca, S, B and Zn deficiency and leaf K and P excess significantly correlated with the enhancement of fire blight severity index (FBSI). The reduction of global nutrient imbalance index (CND-r2) to below 20.38 decreases the severity of fire blight in 'golden delicious' apple orchards. The CND reference norms for a low fire blight severity subpopulation was calculated. CND nutrient index significantly correlated with fire blight severity index. The reduction of global nutrient imbalance index decreases the severity of fire blight. [ABSTRACT FROM AUTHOR]

The present initiative study has been planned to develop a conceptual model for watershed resilience for which rare documents have been reported yet, particularly in developing countries where such studies are necessary. In this vein, different ecological, social, economic, and infrastructural and cultural key domains were applied for the modeling processes of watershed resilience for the Shazand Watershed in Iran. To this end, watershed resilience was firstly conceptualized according to the prevailing conditions for three periods 1986–1998, 1999–2008, and 2009–2016. Accordingly, the watershed health index was used for the ecological dimension, and 13, 8, and 13 criteria were consequently considered for social, economic, and infrastructural and cultural dimensions, respectively. The whole data required for the last three dimensions were collected through the distribution of questionnaires among the stockholders of the Shazand Watershed. The principal component analysis (PCA) method was used to weighting and prioritize the criteria and dimensions. The overall resilience index was ultimately calculated by aggregating all four dimensions using a geometric mean. The effect of each dimension on resilience was also assessed by applying multivariable regression. According to the resilience map of the period 2009–2016, 40%, 9%, 34%, and 17% of the Shazand Watershed has been classified as very low and low resilience, moderate, high, and very high resilience. This study showed that the resilience of the Shazand Watershed has improved over time. The necessity of resilience modeling for practical and integrated management of watersheds was also confirmed during the present research. Recommendations for Resource Managers: Application of resilience zoning results in explaining management strategies.Monitoring the changes in resilience status in certain periods.Improving the level of stakeholders' awareness in dealing with disasters adaptive management. [ABSTRACT FROM AUTHOR]

In seismic methods, pore pressure is estimated by converting seismic velocity into pore pressure and calibrating it with pressure results during the well-testing program. This study has been carried out using post-stack seismic data and sonic and density log data of 6 wells in one of the fields in SW Iran. While an optimum number of attributes is selected, the general regression (GRNN) provides higher accuracy than back propagation (BPNN) at the initial prediction stages. However, acoustic impedance (AI) is the most applicable seismic attribute used as root and reverses AI for estimating P-wave and density. Using a set of attributes can train the system to estimate the property. The correlation coefficient of actual and predicted P-wave using an AI seismic attribute has been calculated as 0.74 and the multi-attribute technique as 0.79. Also, density and three attributes reach from 0.57 to 0.60, which shows a better relationship between seismic attributes and density. After determining optimum layers with the principal component analysis (PCA), formation pressure was modeled with the feed forward–back propagation (FFBP-ANN) method. Five information layers, including gamma, VP, AI, density, and overburden pressure, have the most linear convergence with the initial pressure model and are used to modify the ANN model of effective pressure. [ABSTRACT FROM AUTHOR]

Every year, droughts cause a lot of damage in arid and semi-arid regions. Based on drought indicators, the study aimed to determine and predict drought in southern Iran. In this study, we used Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Temperature Vegetation Dryness Index (TVDI) drought indices prepared from MODIS image and Standardized Precipitation Index (SPI), and RDI prepared from 18 meteorological stations from 2000 to 2020 (March to September) in time scale 1, 3, 6, and 9, 12-months. In addition to Principal component analysis (PCA), the most relevant remote sensing drought indices are also selected using meteorological drought indices. The drought situation for the region is also forecast using the Markov and CA-Markov methods. Further, the multilayer perceptron (MLP) method is used to predict drought and replace meteorological drought indices with remote sensing drought indices. It also examined how drought conditions relate to landforms in the study area. the results of indices from 2000 to 2020 showed that currently, the drought situation is more severe in the eastern half of the study area. According to PCA results in the study area, SPI and TVDI are the most relevant drought and meteorological indices to investigate the drought. SPI drought index can also be predicted using the MLP method using TVDI with an accuracy of about 0.8. Based on the CA-Markov the study area will suffer from drought in 2040. So that 34% of the area is classified as arid in 2040. According to the results of the relationship between landforms and drought indices, ridges and peaks have the least drought compared to other landforms. So, it is clear that the region is at risk of drought in the future, but if management measures are taken, damage should be prevented. [ABSTRACT FROM AUTHOR]

Construction delays are among the industry's most significant challenges, especially in the infrastructure sector, where delays can have serious socio-economic consequences. Recently, advances in deep learning (DL) have opened up new possibilities for tackling complex issues more efficiently. This study aims to evaluate the potential of deep neural networks in predicting the level of delay in Iranian dam construction projects. As the first step, 65 delay risk factors were identified through a comprehensive literature review and interviews. Then risk scores for 53 completed dam projects in Iran were determined through a questionnaire survey. Subsequently, the most significant latent features were extracted using principal component analysis (PCA). The resultant variables were combined with two project characteristics to develop the input dataset. Finally, the resulting dataset was used to develop a deep multilayer perceptron neural network (MLP-NN) model to predict project delays. The prediction performance of the deep-MLP model was then evaluated and compared to that of the best delay prediction models found in previous studies. The three-times repeated stratified five-fold cross-validation results demonstrated that the proposed deep-NN model outperformed all previous approaches for delay prediction on all performance metrics. This study also explores the effectiveness of combining delay risk factors with project characteristics to train the predictive model. According to the results, adding project characteristic factors to the training dataset significantly improved the prediction performance of deep-MLP. The work presented here can assist managers of future dam constructions in the early stages of the project in selecting and prioritizing projects within a portfolio and allocating a sufficient buffer to ensure the project's timely completion. [ABSTRACT FROM AUTHOR]

Fig canker is a serious concern in the major fig‐growing region of Iran, Fars Province. The disease reduces yield and the lifespan of fig trees, particularly the dominant commercial dried fig cultivar, Sabz. Four consecutive years of surveys (2019–2022) led to the identification of four distinct canker types (A–D) in infected fig orchards in southern Iran. Morphological identification and multiple gene phylogenetic analyses indicated that 218 (>66% of the total) isolates from infected fig cultivars belonged to Diaporthe cinerascens, all of which were recovered from Type A cankers, that is, trunk cankers with zonation. Based on scales developed by the principal component analysis (PCA) approach of pathogenicity characteristics, D. cinerascens isolates were clustered into three groups: low, moderate and highly aggressive. The susceptibility of 10 drought and/or salinity‐tolerant fig cultivars to a highly aggressive D. cinerascens isolate was evaluated based on five pathogenicity characteristics. These cultivars were clustered into nonsusceptible (cultivar Matti), semisusceptible (the majority of the cultivars) and susceptible (cultivars Dehdez and Gilasi). The present study is the first attempt to assess the response of fig cultivars to the most significant and common pathogen causing fig canker, D. cinerascens, by phenotyping techniques. [ABSTRACT FROM AUTHOR]

Recently, the application of Fourier transform infrared (FT‐IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT‐IR spectra were acquired in the spectral range 1000–8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R2 =.84, RMSE = 0.29), acidity (R2 =.71, RMSE = 0.0004), phenol (R2 =.35, RMSE = 0.19), total anthocyanin (R2 =.93, RMSE = 5.85), and browning (R2 =.89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R2 =.98, RMSE = 0.003) and pH (R2 =.99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT‐IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike's Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed. [ABSTRACT FROM AUTHOR]

The present study aimed to develop our understanding of various aspects of EFL teachers’ perceptions of professional development in Iran using mixed-methods design. After a review of current literature and questionnaires, in the qualitative phase of the study, interviews were conducted with 12 EFL teachers and teaching experts. The data obtained from the interviews was subject to content analysis and the results revealed 4 major themes. In the quantitative phase, based on the findings of the qualitative phase and the supporting literature, a 74-item questionnaire was constructed and administered to 250 participants, after pilot testing and reviewing of the items. The quantitative data was analyzed through Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA) and a 4-factor structure was identified. After modifications in the questionnaire, the final version of the questionnaire was administered again to the participants and Confirmatory Factor Analysis (CFA) confirmed the construct validity of the EFL TPPD questionnaire and the four components. The results revealed that professional development activities, benefits, needs and barriers are the major dimensions of Iranian EFL teachers’ perceptions of professional development. The proposed model can have theoretical and practical contributions to EFL teacher professional development. [ABSTRACT FROM AUTHOR]

Macroinvertebrate communities have been influenced by chemical substances, originated from petrochemical developments, that caused many problems in the marine biota. This study investigated the surface sediments of Nayband National Park and Bay (northern Persian Gulf) for polycyclic aromatic hydrocarbons (PAHs) and total petroleum hydrocarbons (TPHs) in terms of their distribution, source, and impacts on benthic macroinvertebrate assemblages. To this end, a total of 180 surface sediment samples from 20 stations were collected using Van-Veen grab sampler during winter 2018. The concentration of PAHs, TPHs, total organic carbon (TOC). and total organic matter (TOM) were evaluated, and grain size measurements were conducted on sediment samples in this study. Benthic macroinvertebrates were then identified in terms of presence and distribution. The results indicated that coarse granulometric fractions of sands were prevalence in all samples stations. The total concentration of PAHs ranged from 47.57 to 657.68 ng/g and TPHs 5.72 to 42.16 µg/g dw. The risk of ΣPAHs and TPHs in the sediments was relatively low to moderate according to the sediment quality guidelines. Analysis of the results revealed a significant negative correlation between ΣPAHs (R-value = − 0.917; P < 0.01), TPHs (R-value = − 0.849; P < 0.01) and macrofaunal abundance. Findings demonstrated that the species richness and abundance were at the lowest levels in stations where concentrations of PAHs, TPHs, TOC, and TOM were in the highest values, suggesting that these contaminants could negatively influence the benthic organisms in Nayband National Park and Bay. The results of correspondence analysis (CA) and principal component analysis (PCA) analysis showed that sedimentary habitats in Nayband National Park and Bay are being negatively affected by PAHs and TPHs, released from Pars Special Economic Energy Zone (PSEEZ). Moreover, the marine biotic index (AMBI) and Shannon-Weiner Diversity (H′) results suggest that Nayband National Park and Bay can be classified as slightly to moderate polluted area. In conclusion, Northern Persian Gulf is significantly affected by oil industry developments and petrochemical activities. The unique ecosystem like Nayband National Park and Bay has been in a cautious status in terms of the PSEEZ pollutants and the levels of PAHs and TPHs concentration, warning that urgent environmental programs should be considered to protect the diversity and ecology of this valuable marine systems. [ABSTRACT FROM AUTHOR]

Pomegranate has been considered a medicinal plant due to its rich nutrients and bioactive compounds. Since environmental conditions affect the amount and composition of metabolites, selecting suitable locations for cultivation would be vital to achieve optimal production. In this study, data on the diversity of targeted metabolites and morphological traits of 152 Iranian pomegranate genotypes were collected and combined in order to establish the first core collection. The multivariate analyses were conducted including principal component analysis (PCA), and cluster analysis. In addition, the current and future geographical distribution of pomegranate in Iran was predicted to identify suitable locations using the MaxEnt model. The results showed high diversity in the studied morphological and metabolic traits. The PCA results indicated that FFS, NFT, JA, and AA are the most important traits in discriminating the studied genotypes. A constructed core collection using maximization strategy consisted of 20 genotypes and accounted for 13.16% of the entire collection. Shannon-Weaver diversity index of a core collection was similar or greater than the entire collection. Evaluation of the core collection using four parameters of MD, VD, CR, and VR also indicated the maintenance of the genetic diversity of the original set. According to the MaxEnt model, altitude, average temperature of coldest quarter, and isothertmality were the key factors for the distribution of pomegranate. The most suitable areas for pomegranate cultivation were also determined which were located in arid and semi-arid regions of Iran. The geographic distribution of pomegranate in the future showed that the main provinces of pomegranate cultivation would be less affected by climatic conditions by the middle of the century. The results of this study provide valuable information for selection of elite genotypes to develop the breeding programs to obtain the cultivars with the highest levels of metabolic compounds for pharmaceutical purposes, as well as identification of the most suitable agro-ecological zones for orchard establishment. [ABSTRACT FROM AUTHOR]

Analyzing and classifying atmospheric circulation patterns (CPs) is useful for studying climate variability. These classifications can effectively identify the links between large-scale and regional-local scale processes. This work uses the historical (1975–2014) and projected (2015–2054) simulations of the MPI-ESM1-2-HR model to reproduce the CPs over the Middle East and Iran. Eighteen CPs were identified based on the geopotential height (GPH) of 500 hPa data from Coupled Model Intercomparison Project Phase 6 (CMIP6) in SSP1-2.6, SSP3-7.0, and SSP5-8.5. The method of principal component analysis (PCA) and k-means clustering was used. Then, the possible variability of each pattern in the surface and mid-level of the atmosphere and their expected changes in the frequency of CPs in global warming scenarios were investigated. This research showed that CPs 3, 6, and 11 happen during warm months of the year. The surface thermal low pressure is associated with the subtropical high in the atmosphere mid-level. According to the intensity of the low and the northward development, or the orbital expansion of the subtropical high, this pattern has an increasing (CPs 3 and 6) or decreasing (CP11) trend in the future period. CPs 1 and 12 occur during cold months. In CP1, dynamic high pressure prevails over Iran. However, in CP12, Iran is affected by high pressure from southeastern Europe. They will decrease in future projections. CPs 7 and 16, which often occur in the transition season (spring), show an increase in the projected patterns. CP 18 occurs throughout the year, but its highest frequency is in autumn, and the frequency of occurrence decreases. An increase in 500 hPa geopotential height over the Arabian Sea in all 18 classes and all three SSPs is predicted for future periods. Analysis of the obtained weather types indicates the identification of all effective atmospheric circulation patterns in the study area so that the behavior and frequency of each pattern explain the prevailing atmospheric phenomena in this region. [ABSTRACT FROM AUTHOR]

Univariate drought indicators are insufficient for characterizing the complicated effects and conditions of droughts. Accordingly, this study aimed to introduce and assess a composite drought index called the Integrated Drought Index (IDI), composed of the most important water balance variables including, temperature, precipitation, streamflow, and soil moisture to simultaneously monitor hydrological, agricultural, and meteorological drought. To this end, four widely used linear and non-linear combination approaches—namely the kernel mean component analysis (KMCA), copula function (CF), entropy weighting (EW), and the principal component analysis (PCA)—were used here, whose products are called IDI-KMCA, IDI-CF, IDI-EW, and IDI-PCA, respectively. The research data were extracted from ERA5 (ECMWF Reanalysis v5) datasets on a monthly scale for the 1979–2020 period. According to the findings, all proposed composite indices exhibited a mostly similar variation pattern as the individual indices and performed well in monitoring drought conditions—except for IDI-CF, which slightly deviated from the pattern during the 1989–1990 period. High values of the index of agreement (with the average values ranging between 0.5 and 0.9) and correlation coefficient (with the average values ranging between 0.7 and 0.9) also suggested a good agreement among the proposed composite indices. Since climate and hydrologic conditions in the region were not complex, they evaluated the same drought conditions through linear and non-linear approaches. In addition, Frank functions were selected to derive the joint distribution functions of drought characteristics for bi-variate and tri-variate functions. Finally, considering the spatial distribution of the drought return period, the probability of mild droughts remained the same under bi-variate and tri-variate conditions, whereas the occurrence probability of extreme drought changed (increasing and decreasing in the case of "and" and "or"). [ABSTRACT FROM AUTHOR]

The aim of this study was to assess soil quality and identify main indicators and their critical limits as a function of relative rice yield in northern Iran. In present study, 60 topsoil (0–30 cm) samples were collected and 18 physical, chemical, and biological soil attributes were measured and analyzed. Based on the mean rice yield obtained from sampling sites, paddy fields were divided into fields with low (< 4.5 t ha−1) and high (≥ 4.5 t ha−1) productivity. Using the principal component analysis (PCA), among 18 soil indicators, 4 indicators were selected as the minimum dataset (MDS) including soil organic carbon (OC), urease activity, bulk density (BD), and available Zn (AZn). The upper and lower limits of MDS indicators and soil quality index (SQI) were defined using scatterplot. The results showed that the mean SQI of high productivity fields (0.95) was significantly higher than that in low productivity fields (0.77). The upper and lower limits for soil OC were 3.5 and 1.0 (g 100 g−1), urease activity 84 and 43 (μg NH4 g soil−1 2 h−1), BD 1.84 and 1.60 (g cm−3), and AZn 2.0 and 0.6 (mg kg−1), respectively. The soil quality assessment using SQI accounted for 52% of the rice yield variation. Thus, management practices and mitigation of soil limiting factors should be comprehensively investigated to ensure sustainable rice production in the paddy fields of northern Iran. [ABSTRACT FROM AUTHOR]

The citrus decline is a major threat to citrus production in the southern region of Iran. This study aimed to determine the relationship between the leaf nutrient indices and the citrus decline severity index in Valencia orange (Citrus sinensis L. Osbeck) orchards. The multivariate compositional nutrient diagnosis (CND) was considered to evaluate the relation between nutrient indices and susceptibility of 235 commercial Valencia orange to citrus decline severity index. The CND reference norms for d = 11 leaf nutrients in a low-citrus decline severity subpopulation of 'Valencia' orange was determined as V*N = 2.967 ± 0.127, V*P = 0.288 ± 0.156, V*K = 2.795 ± 0.125, V*Ca = 2.600 ± 0.269, V*Mg = 1.331 ± 0.095, V*S = 0.930 ± 0.226, V*Fe = −1.459 ± 0.292, V*Mn = −3.695 ± 0.285, V*Zn = −4.035 ± 0.249, V*Cu = −5.111 ± 0.220, V*B = −3.219 ± 0.177, and V*Rd = 6.609 ± 0.116. These norms were used to determine the nutrient indices in the high-citrus decline severity subpopulation of 'Valencia' orange orchards. According to the principal component analysis and nutrient indices, the deficiency of leaf Ca, B, S, and Mg concentration and excess of P, N, Mn, K, and Cu had a significant correlation with the increment of citrus decline severity index. The balanced nutrition management based on these CND norms could reduce the severity of the citrus decline in Valencia orange orchards. [ABSTRACT FROM AUTHOR]

Findings were inconsistent regarding the superiority of using recently introduced hybrid methods to derive DPs compared to widely used statistical methods like principal component analysis (PCA) in assessing dietary patterns and their association with type 2 diabetes mellitus (T2DM). We aimed to investigate the association between DPs extracted using principal component analysis (PCA), partial least-squares (PLS), and reduced-rank regressions (RRR) in identifying DPs associated with T2DM risk. The study was conducted in the context of two cohort studies accomplished in central Iran. Dietary intake data were collected by food frequency questionnaires (FFQs). DPs were derived by using PCA, PLS, and RRR methods considering. The association between DPs with the risk of T2DM was assessed using log-binomial logistic regression test. A total of 8667 participants aged 20–70 years were included in this study. In the multivariate-adjusted models, RRR-DP3 characterized by high intake of fruits, tomatoes, vegetable oils, and refined grains and low intake of processed meats, organ meats, margarine, and hydrogenated fats was significantly associated with a reduced T2DM risk (Q5 vs Q1: RR 0.540, 95% CI 0.33–0.87, P-trend = 0.020). No significant highest-lowest or trend association was observed between DPs derived using PCA or PLS and T2DM. The findings indicate that RRR method was more promising in identifying DPs that are related to T2DM risk compared to PCA and PLS methods. [ABSTRACT FROM AUTHOR]

Background: Crown imperial (Fritillaria imperialis L.) is a threatened bulbous plant which has great ornamental and medicinal values and importance. In the present study, a total of 100 specimens of wild-growing F. imperialis from 10 natural areas of Markazi province, Iran, representing one of the main centers of genetic diversity of this species, were evaluated using 37 phenotypic attributes during April 2021. Results: High level of genetic variation within populations (75%) and low levels of genetic variation among populations (25%) was revealed. The highest coefficient of variation (CV) was found in leaf trichome (82.00%) and then margin of crown leaves (80.44%). In addition, flower color (CV = 50.86%), flower number (CV = 44.61%), peduncle diameter (CV = 33.44%), and plant length (CV = 32.55%)—all important from an ornamental point of view- showed relatively high CV values. The CV was the lowest for flower shape, filament color, bulb shape, bulblet number, and floral scent. Ward cluster analysis identified two main clusters, containing 14 and 86 specimens, respectively. The first group consisted mainly of specimens from the adjacent Shahbaz and Rasvand populations. According to the principal component analysis (PCA), the first six components of data accounted for 88.36% of total variance. The Shahbaz-1, Shahbaz-2, Shahbaz-6, Shahbaz-7, Shahbaz-9, and Bolagh-8 specimens showed the highest variation and were separated from others, which they can be used further in breeding programs, while Sarchal-2, Bolagh-3, and Chepeqli-4 specimens showed the lowest variability. Moreover, the studied populations were clustered into four distinct groups, each including populations that were geographically close to one another. Conclusions: Although the examined specimens revealed high genetic diversity herein, the results indicated that wild-growing populations of F. imperialis are still at risk suffering from overcollection in the most of studied areas, especially in Deh-Sad and Tureh. [ABSTRACT FROM AUTHOR]

This article investigates the relationships between soil characteristics (physical and chemical) and wood properties of Persian oak in three different elevation sites. For this purpose, 27 trees were randomly chosen and cut in Zagros forests in western Iran. The test samples were prepared at the stem (breast height) to examine physical and biometrical properties. For each elevation site, four soil samples were obtained at a 0-20 cm soil depth under the canopy of each tree to measure soil properties, including clay, silt soil, sand soil, electrical conductivity, pH, nitrogen, phosphorus, potassium, and organic matter content. Then, the relationship of soil and wood properties was determined by principal component analysis. Results specified that there are a positive correlation between wood density and volumetric swelling with clay and available potassium. Moreover, the results revealed a positive correlation between fiber length, cell wall thickness, and fiber diameter with electrical conductivity, sand percentage, and total nitrogen content, respectively. [ABSTRACT FROM AUTHOR]

This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals. [ABSTRACT FROM AUTHOR]

There is an increasing interest in the use of nano-fertilizer as an alternative to traditional fertilizers, in order to increase the secondary metabolites in medicinal plants. The present study was conducted to observe the changes in morphological and biochemical properties of three populations of Colchicum speciosum L., from Iran (Filband, Sangdarka, and Kelerd), upon conventional (1.1 and 2.2 mg L-1) and nano-N (1.5 and 3 mg L-1) fertilizers based on completely randomized design (CRD). The anthocyanin and phenolic contents of leaves and corms were determined with a spectrophotometer and corm colchicine was extracted by high-performance liquid chromatography (HPLC). The results represented different responses of morphological and biochemical attributes to populations due to their different origins. Filband with higher altitude showed increased leaf area, plant length, corn weight and diameter when treated with N fertilizers. Increased total phenolic component (TPC) in leaves and corm and also leaf total flavonoids component (TFC) were reported in Filband upon N fertilizers. All fertilizers led to decreased leaf anthocyanin when N fertilizers were applied. A noticeable increase in colchicine was obtained in Kelerd and Sangdarka when plants were treated with 1.5 mg L-1 nano-N fertilizer. Heat map analysis of corm anthocyanin, leaf anthocyanin, leaf TPC, leaf TPC, and colchicine amount showed higher variability under the treatments. According to principal component analysis (PCA), corm TPC, leaf TFC, corm TFC, leaf area, and corm weight were specific traits of Kelerd. In conclusion, different responses of morphological and biochemical traits were reported between populations, and N fertilizers exerted diverse effects on these traits. The population from higher altitudes showed higher phenolic content and lower anthocyanin accumulation under conventional and nano-N fertilizers. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the effect of a lithological factor on the distribution of elements in the soil of the surrounding areas. For this purpose, the Zozan plain was selected which is located in the southeastern part of Khorasan Razavi province in the Sangan Khaf region of Iran between 59° 30' to 60° 00' east longitude and 34° 00' to 34° 30' north latitude. Since lithologic and anthropogenic factors control the distribution of elements and soil contamination in a region, this study investigates the effect of these factors, especially lithologic units, on the distribution of pollutants and contamination in the Zuzan Plain. A total of 53 soil samples were taken from the area and analyzed by the inductively coupled plasma method to achieve the research objectives. Investigation of correlations between soil chemical parameters based on Pearson correlation coefficients, cluster analysis, and principal component analysis indicated two main clusters. The results of geochemical studies on the soil of the region showed that the soils of the area have a low enrichment in terms of the mean enrichment factor (EF) compared to calcium elements. The geo-accumulation index (Igeo) indicated that in soils of the Zuzan area, only calcium element in the non-infected class was slightly infected. The soil contamination coefficients showed that the main contamination of the soil was caused by calcium and, to a lesser extent, by chromium and lead. The results of the contamination degree of the samples in the study area indicated that 12 samples were in the acceptable class while the rest of the samples fell in the middle class. A comparison of the data obtained from the analysis of the rock units around the plain showed that the samples of the Zuzan plain were influenced by natural factors and that the lithogenic units of the area controlled the main distribution of the elements. [ABSTRACT FROM AUTHOR]