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Given the substantial effects of agricultural practices on the environment, this paper introduces a novel stakeholder-based framework for assessing the ecosystem services (ESs) provided by agricultural areas. Ecosystem services include essential functions such as water supply, food production, carbon storage, soil erosion control, and habitat support. In addition to ESs, water footprint is also taken into account to evaluate the impacts of agricultural activities on water resources. Some of the mentioned ESs are assessed using the Soil and Water Assessment Tool (SWAT). Then, by extending and combining the Conflict Resolution Model with the Composition of Probabilistic Preferences (CRMCPP) method and the leader-follower game (LFG), while considering the hierarchical structure of decision-makers, the best scenario for enhancing the ESs is selected. The Zarrinehroud River Basin (ZRB) in Iran has been chosen as a case study to evaluate the performance of the proposed framework, as this basin is vital for supplying water to Lake Urmia, the largest hypersaline lake in the Middle East. In this paper, 16 Water and Environmental Resources Management (WERM) scenarios have been defined according to the Urmia Lake Restoration National Committee (ULRNC) projects. Then, the mentioned ESs have been evaluated under different WERM scenarios. Ultimately, by utilizing the CRMCPP-LFG method and taking into account the hierarchical structure of decision-makers, we can identify the optimal WERM scenario. The criteria for making this decision include various factors, such as ecosystem services and the costs involved in implementing the WERM scenarios. In the selected scenario, the average water inflow into Lake Urmia is projected to rise to 1329 million cubic meters per year, which is 6.3% more than the average inflow in the current condition. Key initiatives in this scenario include reducing cultivated areas, altering irrigation methods, changing crop patterns, and incorporating water-efficient plant species., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This work reports a characterization of microplastics (MPs) and microrubbers (MRs) such as color, size, shape, and the chemical composition in snowfall in central Iran (Qom) at five sites. Identification of MPs and MRs involved using a stereo microscope to evaluate physical characteristics such as thickness, shininess, hardness, and reaction to a hot needle. HYSPLIT trajectory and end-member microplastic (PM) mixing models are used to determine the contribution of local and remote sources to MPs. The mean (±SD) abundance of MPs and MRs is 23.8 ± 15.87 (MP/L) and 1.26 ± 1.16 (MR/L), respectively, whereas the mean (±SD) abundance of MPs and MRs is 30.7 ± 19.36 (MP/m 2 ) and 1.58 ± 1.39 (MR/m 2 ), respectively. The frequency of MP colors in order are: black (63.8%) > red (12.8%) > blue (11 %) > white (9.1%) > green (2.1%) > yellow (1.3%). About 71.6% and 3.6% of the identified MPs are 250-500 μm and >1000 μm, respectively. Snow samples contain different identified polymers such as polyethylene terephthalate (PET), nylon, viscose, polypropylene (PP), polyethylene (PE), polystyrene (PS), and polyvinyl chloride (PVC). Analysis reveals that 90% of Qom city's snowy day winds come from local sources. The high MP levels in are due to human activities from local origins according to end-member MP mixing model. Further study is needed to understand the potential impacts of MPs and MRs on ecosystems and human health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Due to industrial development, expansion of communities, and attention to sustainable development, sustainable energy supply has become a big challenge for communities. In this regard, the development and use of Renewable Energy (RE) are considered due to reducing the harmful environmental effects of fossil fuels. Improving the efficiency of the Renewable Energy Supply Chain (RESC) is important for using RE. To improve the performance and efficiency of RESC, it is necessary to use emerging technologies such as the Internet of Things (IoT) and its integration with the principles of the Circular Economy (CE). Therefore, this study proposes integrating IoT and CE for sustainable development and resource management in RESC. Also, this research provides a hybrid decision framework to assess the challenges of IoT and CE in the RESC of Iran. The CRiteria Importance Through Intercriteria Correlation (CRITIC) technique is used to specify the importance of the criteria. The Fuzzy Evaluation Based on Distance from Average Solution (FEDAS) technique ranks the challenges. The findings indicated that considering the cost of investment, the rate of return on investment, and the productivity rate were the most important sub-criteria with values of 0.149, 0.129, and 0.106 respectively. Then, the sensitivity of the results is examined and the validation of the findings is analyzed with decision-making methods. The results indicate the high priority of the challenge related to transparency in the implementation procedures of IoT and RE projects and information dissemination protocols, the development of guidelines for the integration of IoT in other systems in the information network, and the amount of investment and lack of access to financial resources. This study provided practical insights for RE development based on IoT and CE capabilities for energy planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: It is essential to control the microbiology of dental unit water lines (DUWs) to prevent the spread of nontuberculous mycobacteria (NTM) and associated oral diseases. Therefore, the objective of this study was to quantify the presence of NTM in the water of 112 DUWs from dental centers and 57 DUWs from individual dental offices in Tehran, Iran., Methods: A total of 169 water samples were collected from DUWs. After filtration through a 0.45 μm membrane, the samples were decontaminated with 0.005 % cetylpyridinium chloride and then cultured on two Lowenstein-Jensen media, incubated at 25 °C and 37 °C for 8 weeks. Positive cultures for mycobacteria were analyzed using phenotypic tests, and the NTM species were identified through 16S rDNA, rpoB, and hsp65 genes analysis. Drug resistance was also assessed., Results: Of the total isolates, 38 (34.5 %) were classified as slow-growing mycobacteria (SGM), while 72 (65.5 %) were categorized as rapid-growing mycobacteria (RGM). NTM isolates were identified using molecular tests, including M. chelonae, M. abscessus, M. lentiflavum, M. mucogenicum, M. fortuitum, M. kansasii, M. simiae, M. gordonae, M. conceptionense, M. phocaicum, M. porcinum, and M. aurum. The NTM counts ranged from 50 to >500 CFU/500 mL across these 188 samples, with a median of 350 CFU/500 mL. Additionally, we reported two cases of intraoral infection caused by M. abscessus and M. chelonae, where the source of infection was traced to NTM-contaminated DUWs., Conclusions: The study found that most DUWs contained water contaminated with NTM, posing a potential health risk to humans. This research underscores the necessity of stringent quality control and certification of DUW water, with particular emphasis on ensuring the absence of NTM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this work, the antibiotic resistance, biofilm formation capability, and clonal relatedness of 50 A. baumannii isolates collected from three hospitals in Ardabil city, Iran, were evaluated. Antibiotic sensitivity and biofilm formation of isolates were determined by disk diffusion and microtiter-plate methods, respectively. Molecular typing of isolates was also performed using repetitive sequence-based PCR (REP-PCR). The majority of isolates were resistant to cephems, aminoglycosides, and carbapenems, with 80 % classified as multi-drug resistant (MDR). While, only isolates collected from blood and tracheal were resistant to colistin. Additionally, 42 isolates (84 %) had biofilm formation capability. According to rep-PCR results, 34 isolates showed similar banding patterns, while 16 isolates had unique banding patterns. Finally, based on the molecular analysis, there was a direct relationship between biofilm formation and the antibiotic resistance of isolates. In other words, MDR isolates had a higher ability to form biofilm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Water scarcity has become a serious challenge in many parts of the world due to increasing demands and the impacts of climate change. The agriculture sector globally accounts for a major portion of water consumption, yet it also holds substantial potential for water conservation. Among the most effective ways to conserve water is to cultivate low-water-demanding crops, such as medicinal plants (MPs), instead of water-demanding crops (WDC). However, the voluntary participation of farmers, largely influenced by socio-psychological drivers, is crucial for successfully implementing most water conservation programs and needs to be addressed. Therefore, the main objectives of this paper were: (1) to identify the determinants that explain farmers' intention and behavior in cultivating MPs instead of WDC; and (2) to examine the effectiveness and performance of an extended version of the theory of planned behavior (TPB) in predicting farmers' intention and behavior toward cultivating MPs by innovatively incorporating four new variables into the original TPB model: perceived barriers, moral norms, compatibility, and relative advantage. The applicability of the theoretical framework was evaluated in the Sojasroud Plain, Zanjan province, Iran. The results of the structural equation modeling revealed that: (1) farmers' intention to cultivate MPs instead of WDC is significantly influenced by perceived barriers, moral norms, subjective norms, and perceived behavior control (the strongest predictor); and (2) farmers' behavior in cultivating MPs instead of WDC is predicted by relative advantage, compatibility, and intention (the most prominent determinant). The R 2 values for predicting intention and behavior were 55% and 53%, respectively. Based on the results, some practical policies were proposed to increase the cultivation of MPs in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Lakes are indispensable to ecological balance, supporting biodiversity and human welfare. Despite their importance, lakes around the globe are diminishing at an alarming rate, presenting substantial environmental challenges exacerbated by climate variability and human-induced pressures, such as unsustainable land management and excessive water withdrawals. This study offers an extensive analysis of the desiccation phenomena affecting major lakes on the Iranian plateau, dissecting the complex interplay of contributing factors. We examine meteorological, hydrological, and agricultural drought conditions using indices like SPI, SDI, and SPEI to gain nuanced insights into drought dynamics. This study sheds light on the factors that contribute to the drying up of lakes and quantifies the impact of anthropogenic and climatic drivers on this phenomenon. The findings indicate that the area of all lakes has declined dramatically after the change point, ranging from 51% in the Urmia Lake to an impressive rate (i.e. 100%) in Southern Hamun. Despite the stable rate of meteorological droughts (increasing in temperature by about 1 °C since the 90s and stable precipitation patterns across most basins), the research highlights the ongoing struggle against severe hydrological and agricultural drought conditions. The study delineates the predominant role of anthropogenic activities in the diminishing lake inflows and the consequential drying of the lake, accounting for approximately 100 % in five of the seven lakes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Group A rotaviruses (RVAs) are a major cause of acute gastroenteritis in children under 5 years of age worldwide. Herein, the genetic sequences of 11 RNA segments from three uncommon G9P[4] RVA strains found in the stool samples of children under 5 years of age in Iran were analyzed using next-generation sequencing (NGS) technology. The genomic constellations of these three uncommon G9P[4] strains indicated the presence of the double and quadruple reassortants of two G9P[4] strains, containing the VP7/NSP2 and VP7/VP2/NSP2/NSP4 genes on a DS-1-like genetic background, respectively. The genome of one strain indicated a Wa-like genetic backbone in a single-reassortant with the VP4 of the DS1-like human strains. With the exception of VP1, VP2, VP7, NSP2, NSP3, and NSP4 genes, which clustered with RVA of human origins belonging to cognate gene sequences of genogroup 1/2 genotypes/lineages, the remaining five genes (VP8/VP4, VP3, VP6, NSP1, NSP5) displayed direct evidence of recombination. It is presumed that the presence of uncommon G9P[4] strains in Iran is not linked to vaccination pressure, but rather to the high prevalence of RVA co-infection or the direct import of these uncommon RVA reassortants strains from other countries (especially those that have implemented RV vaccination)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Choosing the most effective machine learning algorithms is very important in accurately predicting bioavailability of PTEs. Therefore, four machine learning (ML) models including decision tree regression (DTR), random forest regression (RFR), multi-layer perceptron regression (MLPR) and support vector regression (SVR), were used and compared for estimating the selected PTEs bioavailability. For these models, 9 variables (total concentration, pH, EC, OM and five chemical forms F1 to F5 obtained by sequential extraction) in 100 sediment samples were considered. The results showed that contamination level decreases from Zn and Cu to Pb and Mo, but the order of the mobility coefficient of the elements in the sediment follows the trend of zinc > copper > molybdenum > lead, and variation coefficient indicated more variability of spatial distribution for Zn and Cu. Among the four tested models, DTR and RFR performed the best for predicting PTEs bioavailability variations (with roc_auc>0.9, R 2  > 0.8 and MSE>0.5), followed by MLPR and SVR. Furthermore, the relevance of the factors controlling the metals availability, evaluated using the RFR-based feature importance method and Pearson correlation, revealed that the most important physicochemical property for Zn, Cu and Mo bioavailability was pH, whereas for Pb, EC was the determinant factor. In the case of chemical speciation, F5 had an inverse correlation with the target, while F1 and F2 had a direct correlation. These fractions contributed significantly to the prediction results. This study represents the potential successful application of ML to PTEs risk control in sediments and early warning for the surrounding water PTEs contamination., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Milk thistle (Silybum marianum (L.) Gaertn.) is cultivated globally as a valuable medicinal plant. The presence of weeds poses numerous challenges to milk thistle production, making weed management the primary concern in milk thistle fields. Chemical weed management is an economical and promising approach to controlling weeds in cropping systems. Therefore, to investigate the tolerance of milk thistle to soil-applied herbicides, in the spring of 2022, we conducted a pot experiment as a completely randomized factorial design with four replications at the research greenhouse of the University of Birjand, Iran. The applied herbicides included metribuzin, pendimethalin, trifluralin, and ethalfluralin at six doses (0, 50, 75, 100, 125, and 150% of the recommended dose (ai ha -1 )). Herbicide treatments had adverse effects on the root and shoot growth of milk thistle. Compared to the control, ethalfluralin at 150% (-60.1%) and metribuzin at 50% (-13.3%) had the highest and lowest herbicide negative effects on root dry weight, respectively. In contrast to the control, we found that ethalfluralin at 150% (-64.4%) and metribuzin at 50% (-9.3%) of the recommended dose had the highest and lowest impacts on shoot dry weight, respectively. Furthermore, herbicide applications decreased the membrane stability index (MSI) and relative water content (RWC). Root and leaf levels of malondialdehyde (MDA), total phenol, DPPH scavenging, soluble carbohydrates, and proline increased after all herbicide treatments, compared to the control. Metribuzin and pendimethalin had fewer negative effects on milk thistle growth. Consequently, these herbicides could be considered as potential options for weed control in milk thistle fields., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Ticks play an important role in the transmission of parasitic diseases, especially pathogenic protozoa in canine hosts, and it is very important to determine the role and extent of their infection with these pathogens in order to determine important control strategies. This study assessed the molecular prevalence of three protozoan pathogens including Hepatozoon canis, Leishmania spp. and Babesia spp., in ticks using PCR. A total 300 stray dogs were investigated and 691 ticks (171 male, 377 female and 143 nymph) were detected directly from 45 infested dogs. Species, stage of growth, and gender were determined for each tick. DNA extracted from 224 ticks (26 male, 165 female and 33 nymph). The molecular presence of three protozoan pathogens including Hepatozoon spp. (18S rRNA gene), Leishmania infantum (kinetoplastid minicircle DNA) and Babesia spp. (ssrRNA gene) were investigated using PCR method. One species of ticks, Rhipicephalus sanguineus was identified. Two of the target pathogens, Hepatozoon spp. (7/83; 8.43 %) and Babesia spp. (1/83; 1.2 %), were detected by PCR method. Sequence analysis of the ssrRNA gene of detected Babesia spp. showed a close relationship to the deposited strains of Babesia vulpis in the gene bank. To the best of our knowledge, this is the first study to undertake a phylogenetic analysis of H. canis and Babesia spp. in stray dogs in Alborz province, Iran and the first report about molecular detection of Babesia vulpis from tick infesting dogs in Iran. According to the above results, it seems necessary to implement tick control programs in dogs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The word-of-mouth (WOM) marketing process is one of the main means by which consumers obtain information. As a communication channel between consumers in economically developing countries, WOM may contribute to the development of the organic food market. The primary objective of this study is to segment organic saffron consumers in Mashhad, Iran, and determine how they engage in WOM marketing. Data were collected through questionnaires from 13 districts of Mashhad using a stratified sampling method. In this study, 400 organic saffron consumers were grouped using a self-organizing map (SOM) neural network based on consumer neobehavioristic theory, and then, using decision trees, consumer behavior rules were extracted for participating in the WOM for each group. According to the results, less than fifty percent of consumers in each of the four market segments are willing to participate in WOM advertising for organic saffron. A lack of awareness of the characteristics of organic saffron is also found to be the main reason for consumers' reluctance to recommend organic saffron to others. Mass-media advertising is an effective way to raise consumer awareness and influence opinion leaders, ultimately resulting in WOM recommendations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Acanthamoeba spp., are common free-living amoebae found in nature that can serve as reservoirs for certain microorganisms. The SARS-CoV-2 virus is a newly emerged respiratory infection, and the investigation of parasitic infections remains an area of limited research. Given that Acanthamoeba can act as a host for various endosymbiotic microbial pathogens and its pathogenicity assay is not fully understood, this study aimed to identify Acanthamoeba and its bacterial and fungal endosymbionts in patients with chronic respiratory disorders and hospitalized COVID-19 patients in northern Iran. Additionally, a pathogenicity assay was conducted on Acanthamoeba isolates. Urine, nasopharyngeal swab, and respiratory specimens were collected from two groups, and each sample was cultured on 1.5% non-nutrient agar medium. The cultures were then incubated at room temperature and monitored daily for a period of two weeks. Eight Acanthamoeba isolates were identified, and PCR was performed to confirm the presence of amoebae and identify their endosymbionts. Four isolates were found to have bacterial endosymbionts, including Stenotrophomonas maltophilia and Achromobacter sp., while two isolates harbored fungal endosymbionts, including an uncultured fungus and Gloeotinia sp. In the pathogenicity assay, five isolates exhibited a higher degree of pathogenicity compared to the other three. This study provides significant insights into the comorbidity of acanthamoebiasis and COVID-19 on a global scale, and presents the first evidence of Gloeotinia sp. as a fungal endosymbiont. Nevertheless, further research is required to fully comprehend the symbiotic patterns and establish effective treatment protocols., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Pseudomonas bacteria include a variety of species with distinct characteristics. Some species within this genus are known for their ability to stimulate plant growth. Recently, the potential of these bacteria in controlling insect pests has been documented. In this study, 58 bacterial isolates were purified from rhizospheres of wheat, broad bean and canola that were collected from different fields of Khuzestan province in south-west of Iran. With biochemical tests 19 non plant pathogenic pseudomonads strains were detected and their lethal effects on the eggs and larvae of Ephestia keuhniella as an important pest that infests stored products, were evaluated under laboratory conditions. For the bioassays, two concentrations of each strain were administered, and the 5th instar larvae and eggs of the pest were subjected to treatment. Mortality rates were recorded after 24 h. The results showed that all isolated Pseudomonad strains of this study had insecticidal effects against eggs and larvae of E. keuhniella. The strains AWI1, AWI2, AWI7, ABI12, ABI15 and ABI16 displayed the highest mortality rate (91.1 %, 86.2 %, 82.3 %, 84.2, 90.5 % and 90.5 %, respectively). Molecular identification and phylogeny tree according to 16 s rRNA sequencing clarified that AWI1, AWI2 belong to P. plecoglossicida, AWI5 belongs to P. lini, ABI12, ABI15 and ABI16 belong to P. taiwanensis. Moreover, the bacterial efficacy at a suspension concentration of 0.5 OD (80 %) was significantly greater than that at a concentration of 0.2 OD (63.33 %). No significant difference was detected in the response of the pest larvae or eggs to the different strains. Furthermore, olfactory trials revealed that the female parasitoid wasp Habrabracon hebetor actively avoided the infection of the treated larvae by the strains. These findings have practical implications for the development of microbiological pest control strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Cultural landscapes, particularly cities with rich historical and cultural heritage, play a crucial role in bolstering the resilience of local communities. The occurrence of climate change-induced phenomena jeopardizes cultural landscapes, resulting in the deterioration of historical structures, natural landscapes, cultural heritage, the economy, and the livelihoods of residents in these areas. Therefore, adopting a resilient approach is essential for the integrated management of cultural landscapes. This study develops a model for enhancing cultural landscape resilience to climate change in Nishapur, a historical and cultural city in Iran. Through desk studies, factors affecting the resilience of cultural landscapes to climate change were extracted in the context of developing countries. Subsequently, a model was developed based on the frequency of the occurrence of dimensions and indicators. Snowball sampling was used to distribute questionnaires to 310 members of the academic and professional communities in the field. Next, confirmatory factor analysis (CFA) was conducted using IBM SEM-AMOS to analyze the data and measure the reliability and validity of the model. The findings indicate that the driving factors of change, such as changes in livelihood and social issues, historical fabric and physical environment, natural hazards, biodiversity patterns, and management patterns, can significantly affect the resilience of cultural landscapes to climate change. The developed model can contribute to policymaking in various fields, including urban design and planning, economics, sociology, and cultural heritage conservation. This can play a vital role in creating cultural landscapes that are resilient to the increasing impacts of climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Medication adherence is of utmost importance in achieving the desired therapeutic outcome and effectively managing seizures in patients with epilepsy (PWE). It is imperative to recognize self-esteem as a psychological determinant that potentially influences the optimal compliance with anti-seizure medications (ASMs) among PWE. The objective of this study was to explore medication adherence and its relationship with self-esteem among individuals diagnosed with epilepsy in Isfahan, Iran., Methods: This descriptive-analytical study was conducted in the year 2021, encompassing a cohort of 250 PWE who were referred to designated medical facilities in Isfahan, Iran, and were selected by the consecutive sampling technique. A 3-part instrument including demographic components, the Rosenberg Self-Esteem Scale, and the Morissky Drug Adherence Questionnaire employed for data collection., Results: The mean and standard deviation of adherence to the medicinal regimen in the participants were 6.9 ± 2.02, and 46.4 % had a low level of adherence to the medication regimen (total score 0-6). At the same time, the mean and standard deviation of self-esteem in these patients was 5.11 ± 2.11. There was a statistically significant and direct correlation between adherence to the prescribed drug regimen and self-esteem (rs = 0.464, p = 0.00)., Conclusion: Based on the findings of the study that showed a statistically significant and positive correlation between self-esteem and adherence to the medication regimen, it is advisable to enhance and advocate for these factors in PWE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the attentive interpretable tabular learning (TabNet) model through three swarm-based metaheuristic algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and hunger games search (HGS). A spatial database incorporating dust occurrence areas was created using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2022, including 15 influential criteria related to climate, soil, topography, and land cover. Four models were employed for modeling and DSM generation: TabNet, TabNet-PSO, TabNet-GWO, and TabNet-HGS. Evaluation of the modeling results using performance metrics indicated that the TabNet-HGS model outperformed the other models in both training (mean absolute error (MAE) = 0.055, root-mean-square error (RMSE) = 0.1, coefficient of determination (R 2 ) = 0.959), and testing (MAE = 0.063, RMSE = 0.114, R 2  = 0.947) data. Following TabNet-HGS, the TabNet-PSO, TabNet-GWO, and TabNet models demonstrated progressively lower accuracy. The validation of the DSM was performed by assessing receiver operating characteristic (ROC) curves, revealing that the TabNet-HGS, TabNet-PSO, TabNet-GWO, and TabNet models exhibited the highest modeling accuracy, with corresponding area under the curve (AUC) values of 0.994, 0.986, 0.98, and 0.832, respectively. These results highlight the enhanced accuracy of dust susceptibility modeling achieved by integrating swarm-based metaheuristic algorithms with the TabNet model. The dust susceptibility map provides valuable insights into the sources, pathways, and impacts of dust particles on the environment and human health in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2-22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Inter-basin water transfer projects are a common method used to balance water resources and meet regional demand, particularly in the drinking water supply sector. The potential failure risk associated with inter-basin water transfer projects was examined using Fault Tree Analysis (FTA) and Fuzzy Fault Tree Analysis (FFTA) methodologies in this study. Additionally, the conversion of Fault Tree models into Bayesian Network (BN) and Fuzzy Bayesian Network (FBN) models was explored. Ten basic events were identified as factors that could affect the success of inter-basin water transfer plans, including socio-political, environmental, water resource, economic, and technical criteria. Fault Tree and Fuzzy Fault Tree models were utilized to conduct a risk analysis, which was then converted into crisp and fuzzy FTA-BN through an integrated approach. This approach was applied to evaluate inter-basin water transfer scenarios from the Great Karun basin to the Central Iran Plateau. The superior scenario among eight water transfer scenarios was found to be water transfer from the Behesht-Abad Basin to Isfahan province and from the Khersan Basin to Kerman and Yazd provinces, with a failure risk of 0.649 and 0.601 respectively, based on the crisp and fuzzy integrated models. Basic events were ranked based on their contribution to the occurrence of the top event using two FIM and BI indices in the Fault Tree model and two indices of MI and SI in the Bayesian Network. Furthermore, after considering the correlation between basic events and risk factors, the risk obtained by crisp and fuzzy integrated models was found to increase to 0.811 and 0.789 respectively. The results of this study demonstrate that an integrated approach can assist decision-makers and stakeholders in evaluating inter-basin water transfer projects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Agricultural production in vertical farms (VF) will play a prominent role in preventing environmental crises, its good governance and maintaining food security for everyone in the world. The ecological footprint in Guilan province significantly exceeds its biological capacity, indicating considerable pressure on its natural resources and ecosystems. This problem, which is mostly due to the use of traditional production methods in the agricultural sector, requires a change in lifestyle and production methods. The purpose of the current research is the feasibility of VF in Guilan province with the help of the Strengths, Weaknesses, Opportunities, and Threats (SWOT) model and Quantitative Strategic Planning Matrix (QSPM). After identifying internal factors (strengths and weaknesses) and external factors (opportunities and threats) affecting production in VF, the necessary strategies were determined and then prioritized using the QSPM matrix. Necessary information through written scientific sources and survey studies based on key questions between two groups of professors of geography and rural planning, water resources engineering, soil, architecture and experts from the plant production department and the head of the environment department, Jihad Agricultural Organization of Guilan province and the organization Food hygiene was achieved. The statistical population participating in the survey was 30 people. The results of the research showed that 7 strategies were formulated based on strengths, weaknesses, opportunities, and threats and were prioritized based on the importance of four strategies in the QSPM table. The first priority for ensuring food security in Guilan province is to focus on increasing agricultural productivity per unit area. This strategy is crucial considering the challenges of low ownership and limited land availability in the region. Thus, enhancing productivity per unit area should be given utmost priority to meet the food demands of the population. The appropriate strategy for VF production in Guilan province is a competitive-aggressive strategy and being in this position requires attracting the necessary funds. This study fills research gaps by providing a comprehensive assessment of the feasibility of vertical farming in Guilan province. The emphasis on methodology, strategic planning, and addressing food security and environmental challenges contributes to the existing body of knowledge. By highlighting the transferability and adaptability of the research findings, other researchers can utilize the methods and adapt the strategies to their own regions, promoting further research and advancement in the field of sustainable agriculture., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this study, an attempt was made to estimate the aggregated proportion of Pap smear test uptake among women in Iran from 2012 to 2022 using meta-analysis. The data collection process involved reviewing records registered in databases between January 1, 2012, and September 11, 2022. The final data was analyzed using random effects models, and potential heterogeneity was assessed using the I2 index. To examine publication bias, Egger's test and Funnel Plot were employed. Sensitivity analysis, specifically single study exclusion Meta-analysis, was conducted to assess the influence of each individual study on the aggregated percentage of Pap smear test uptake. A total of 28,754 women were included across the 33 articles analyzed. The pooled percentage of Pap smear test uptake for both one-time and regular screenings was found to be 46.52% (95% CI: 40.91 to 52.14) and 17.80% (95% CI: 12.42 to 23.18), respectively. No significant evidence of publication bias was detected, although the influence of smaller studies was confirmed. Sensitivity analysis indicated that the overall rate of Pap smear test uptake was not highly sensitive to the results of individual studies. The findings emphasize the low rate of regular Pap smear testing among Iranian women and suggest that tailored interventions considering cultural and geographical factors specific to different provinces in Iran could help increase the utilization of this screening service., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements and remote sensing images, reducing reliance on laboratory testing. Different Kriging techniques were employed to map ground-based measurements and fill data gaps. The methodology was applied to analyze the Maragheh aquifer in northwest Iran, revealing declining groundwater quality due to industrial. discharges and over-extraction. Spatiotemporal analysis indicated a relationship between groundwater depth/quality, precipitation, and temperature. The Root Mean Square Scaled Error (RMSSE) values for all variables ranged from 0.8508 to 1.1688, indicating acceptable performance of the semivariogram models in predicting the variables. Three AI models, namely Feed-Forward Neural Networks (FFNNs), Support Vector Regression (SVR), and Adaptive Neural Fuzzy Inference System (ANFIS), predicted groundwater quality for wet (June) and dry (October) months using input variables such as groundwater depth, temperature, precipitation, Normalized Difference Vegetation Index (NDVI), and Digital Elevation Model (DEM), with Groundwater Quality Index (GWQI) as the target variable. Ensemble methods were employed to combine the outputs of these models, enhancing performance. Results showed strong predictive capabilities, with coefficient of determination values of 0.88 and 0.84 for wet and dry seasons. Ensemble models improved performance by up to 6% and 12% for wet and dry seasons, respectively, potentially advancing groundwater quality modeling in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The stigma toward epilepsy is a daily challenge for people with epilepsy (PWE) and can influence the well-being and prognosis of these individuals. However, the cultural origins of such stigma have sparsely been examined. In this study, we aimed to investigate the classic Persian literature to gain insight into the historical and cultural beliefs and opinions regarding epilepsy as a determinant in developing stigma towards PWE., Methods: In this narrative review of the classic Persian literature, we investigated the opinions and beliefs regarding epilepsy and PWE. In February 2023, we searched https://ganjoor.net (an open-access database of Iranian literature) for the relevant literary materials (poem and prose) using the Persian translation of the terms epilepsy (Sar'e) and epileptic (Masroo'e). Two clinical researchers went through the results and extracted the related materials under the supervision of two experts in Persian literature. A bilingual academic translated the included literary materials from Persian to English., Results: Our search yielded 57 literature materials by 31 poets and writers from the 4th to 14th Hijri centuries, roughly coinciding with the past Gregorian millennium (1000-2000 AC). We classified the literary concepts related to epilepsy stigma into five subgroups: (i) a simile for pain and suffering; (ii) an atonement for blasphemous beliefs or a disease caused by supernatural power; (iii) a sign of madness, insanity, and lack of wisdom; (iv) a celestial observation; and (v) a subject for jokes., Conclusions: Classic Persian literature considered epilepsy as a simile for suffering and a sign of insanity that is caused by blasphemous beliefs or celestial objects, with PWE classified as insane and unwise. While such depictions became less prevalent after the 14th century CE, they can give rise to misconceptions and negative stigma toward PWE and should be addressed in modern culture. Each society should critically investigate its socio-cultural origins of stigma and rectify such misbeliefs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Negative environmental impacts of nitrogen (N) intensive diets have triggered global debates on sustainable nitrogen management. Solutions such as dietary transitions, cropland reallocation and N Regulatory Policy (NRP) have been proposed to mitigate the adverse environmental impacts of N use in food production. However, there is still insufficient understanding of how NRPs could be designed to minimize negative environmental impact across diverse agro-ecological zones without sacrificing human dietary requirements. To increase this understanding, we evaluated the consequences of three NRP scenarios (low, moderate, and high N fertilizer rates) on the amount of livestock and non-livestock diet components as well as the associated N leaching and farmers' Gross Margin (GM) by optimizing the allocation of cropland between food and feed crops. We developed a bio-economic Interval Fuzzy Multi-Objective Programming (bio-economic IFMOP) model for the Zayandeh-Rud river basin, Iran, and a procedure that accounts for annual average availability of calories per capita, calorie sources from livestock and non-livestock components of three dietary preferences, and inequality in calorie distribution. The interaction among soil, climate and weather variability and NRPs across nine sub-regions of the case study region was handled by crop yield simulation using the DSSAT software. The solution of farmers' GM, derived from the optimization problem across possibilities of water fluctuations, was assessed to determine the uncertainty in GM. We also introduced an N leaching per Block of Distributed Calories (BDC) criterion based on solutions of supplied calories and associated N leaching. The upper bound of the moderate NRP scenario resulted in the smallest N leaching per BDC. This corresponded to ∼0.34, ∼0.34, ∼3.77 and 19.00 million BDC of meat, dairy, wheat and potato, respectively. Also, the upper bound of this scenario satisfied the lowest instability in farmers' GM against water fluctuation compared with low and high NRP scenarios. The affordable volume of N leaching per BDC varied across sub-regions between [1.53,3.49], [1.52,3.33], [0.76,0.99] and [0.05,0.08] kg for meat, dairy, wheat and potato, respectively. Our results highlighted both optimistic and pessimistic prospects of producing low N leaching diets. The approach of this study could also be applied to other regions and countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Introduction: It is very important to provide epileptic patients with sufficient knowledge of SUDEP and empower them regarding its prevention. This study aimed to evaluate the effect of the educational intervention of receiving information about SUDEP on medication adherence, anxiety and depression, and the safety of epileptic patients., Patients and Methods: This study was conducted on 60 epilepsy patients referred to the specialized epilepsy clinic of Imam Hossein Hospital in Tehran, Iran, from April 2022 to February 2023. Data were collected by the Morisky medication adherence scale, hospital anxiety and depression scales, and the researcher-made checklists of bathing safety, sleep safety, and patient seizure preparation before and after the intervention. The educational intervention was conducted through the video and pamphlet regarding knowledge about SUDEP. Wilcoxon and paired t-tests were used to compare the data changes following the intervention., Results: Most patients were male, with an age range of 18 to 29 years old. The mean score of anxiety and depression before and after the educational intervention did not show a statistically significant difference (P = 0.928); however, the mean scores of medication adherence, bathing safety, sleep safety, and preparation for seizure after the educational intervention increased significantly (P < 0.05)., Conclusion: Knowledge about SUDEP would be able to encourage epileptic patients to better adhere to medication; and make them empower regarding seizure preparation, bathing safety, and sleep safety., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Groundwater in arid and semi-arid coastal aquifers is vulnerable to seawater intrusion and quality deterioration despite being one of the most reliable sources of water supply due to the increasing number of development plans and competition between water consumers. A multi-dimensional groundwater management framework is developed to trade-off between groundwater abstraction, allocation equity, groundwater quality, and energy considerations in the reverse osmosis (RO) filtration process in the fresh groundwater lens of Kish Island, Iran. An arid island confined in the Persian Gulf is modeled using 3D simulation and three well-occupied multi-objective evolutionary optimization algorithms. Four objectives include: maximizing the groundwater abstraction, minimizing the Gini coefficient (allocation inequity), minimizing the total energy required to pass saline water through the RO membrane to reach the standard total dissolved solids (TDS), and minimizing the average TDS concentration of water abstraction positions from 11 management zones have been considered over a 50-year management horizon. Solutions obtained in the simulation-based constrained multi-objective optimization framework allow managers to choose from 587 Pareto optimal solutions. They provide an abstraction scheme with a range of 1.44 to 4.53 MCM/yr, a Gini coefficient of 0 to 0.98, filtration energy of 988,562 to 1,935,760 kWh/yr, and an average TDS of 19,663 to 21,351 mg/L. The Pareto optimal solutions can help decision-makers decide on the multi-dimensional problems of sustainable coastal groundwater management and show patterns among different objectives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The Irano-Turanian region is one of the world's richest floristic regions and the centre of diversity for numerous xerophytic plant lineages. However, we still have limited knowledge on the timing of evolution and biogeographic history of its flora, and potential drivers of diversification remain underexplored. To fill this knowledge gap, we focus on the Eurasian genus Jurinea (ca. 200 species), one of the largest plant radiations that diversified in the region. We applied a macroevolutionary integrative approach to explicitly test diversification hypotheses and investigate the relative roles of geography vs. ecology and niche conservatism vs. niche lability in speciation processes. To do so, we gathered a sample comprising 77% of total genus richness and obtained data about (1) its phylogenetic history, recovering 502 nuclear loci sequences; (2) growth forms; (3) ecological niche, compiling data of 21 variables for more than 2500 occurrences; and (4) paleoclimatic conditions, to estimate climatic stability. Our results revealed that climate was a key factor in the evolutionary dynamics of Jurinea. The main diversification and biogeographic events that occurred during past climate changes, which led to colder and drier conditions, are the following: (1) the origin of the genus (10.7 Ma); (2) long-distance dispersals from the Iranian Plateau to adjacent regions (∼7-4 Ma); and (3) the diversification shift during Pliocene-Pleistocene Transition (ca. 3 Ma), when net diversification rate almost doubled. Our results supported the pre-adaptation hypothesis, i.e., the evolutionary success of Jurinea was linked to the retention of the ancestral niche adapted to aridity. Interestingly, the paleoclimatic analyses revealed that in the Iranian Plateau long-term climatic stability favoured old-lineage persistence, resulting in current high species richness of semi-arid and cold adapted clades; whereas moderate climate oscillations stimulated allopatric diversification in the lineages distributed in the Circumboreal region. In contrast, growth form lability and high niche disparity among closely related species in the Central Asian clade suggest adaptive radiation to mountain habitats. In sum, the radiation of Jurinea is the result of both adaptive and non-adaptive processes influenced by climatic, orogenic and ecological factors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Microsporidia are eukaryotic obligatory intracellular parasites that infect a wide range of vertebrates and invertebrate hosts. Spores infect target cells of the host by transferring their sporoplasm through a distinctive polar tube. This study investigated how selected chemicals influence the germination of two newly discovered microsporidia species from central-western Iran. Spores of Parathelohania iranica were extracted from infected larvae of Anopheles superpictus s.l. and purified by the Percoll discontinuous density gradient method. Because of the small number of spores per copepod, extraction and purification were not performed for spores of the microsporidium infecting Paracyclops chiltoni. Both spores were exposed to KCl, NaCl, KI, NaI, and H 2 O 2 and the effects of concentration (0.5, 1.5, and 2.5 M), pH (7.0, 9.0, and 11.0), temperature (4 °C and 25 °C), and duration of exposure (10 and 30 min) on spore germination were investigated and compared. This study indicated that the type of the ionic nature of the surrounding environment of spores plays an important role in the release of polar tubes of both microsporidia. Additionally, anions played a more significant role than cations. This effect was directly related to concentration, temperature, and time. However, no specific pattern was recognized at different alkaline pH levels. Hydrogen peroxide was not effective in releasing the polar tubes of the spores of these microsporidia. This study demonstrated the comparative role of some chemicals and the associated factors in the release of the polar tube of two aquatic microsporidia. Future research should examine the practical value of these findings in the mass production of candidate microsporidia for the biological control of pest invertebrate hosts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Global production of plastics has increased dramatically in recent decades and is considered a major threat to marine life and human health due to their stability, persistence, and potential to move through food chains. The study was conducted to detect, identify and quantify microplastics (MP) in the gastrointestinal tract (GI) of some commercial fish species in the North Persian Gulf in Bushehr Province: Psettodes erumei, Sphyraena jello, Sillago sihama, Metapenaeus affinis and Portunus segnis. A total of 216 plastic particles were collected from 102 individuals (72.68% of all sampled individuals; MP prevalence of 85.1% for M. affinis, 80% for P. segnis, 70% for P.erumei, 60.3% for S.sihama, 45.2% for S.jello). The average number of microplastics per organism was 2.26 ± 0.38 MP/ind (considering only species that ingested plastic, n = 102) and 1.51 ± 0.40 pieces/ind (considering all species studied, n = 140). Microfibers accounted for 58.49% of the total microplastics, followed by fragments (33.02%) and pellets (8.49%). The most common color of microplastic was black (52.83%), followed by blue (22.64%) and transparent (15.09%). The length of microplastic ranged from 100 to 5000 μm with an average of 854 ± 312 μm. Microplastics were significantly (p < 0.05) abundant in two shrimp studied: M. affinis and P. segnis (plastic in 80% of individuals studied) and to a lesser extent in the pelagic barracuda fish S. jello (plastic in 45% of individuals studied). The main synthetic polymers identified by Fourier transform infrared spectroscopy (FTIR) were polyethylene (38%), polypropylene (24%), polystyrene (17%), polyethylene terephthalate (11%) and polyamide (10%). The pollutant load index and lifetime accumulation index were calculated to identify the most polluted species and their toxicity to human health. The white shrimp M. affinis was identified as the most polluted and toxic species for MP based on PLI. The present study can provide valuable data for further research and a background for the control and monitoring of this pollutant in the coastal environment of Bushehr province., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The fly ash (FA) from the combustion of heavy oil in power stations is characterized by fine particles containing toxic metals. The sample utilized in this study was gathered from the dust precipitators of seven heavy-oil-consuming Iranian power plants. Substantial quantities of heavy metals, particularly vanadium, iron, and nickel, have been detected in the sample, indicating both its potential utility and hazard to the soil and groundwater. The harmful consequences of FA disposal on the environment have led to the adoption of recycling as a treatment approach in this study. The valorization of FA was investigated by producing nickel ferrite (NiFe 2 O 4 ) and vanadium pentoxide (V 2 O 5 ) through a novel approach using a combination of pyro-hydrometallurgical processes, which resulted in proposing a recycling closed-loop flowsheet. Roasting was first practiced to form NiFe 2 O 4 by reacting the nickel and iron content of the FA. The NiFe 2 O 4 showed a low dissolution against inorganic acids (H 2 SO 4 , HCl, and HNO 3 ). The vanadium content of the FA showed a remarkable recovery in H 2 SO 4 (91%) and HCl (95.6%), while the dissolution of Ni was limited to 16.85% and 17.5%, respectively. The produced NiFe 2 O 4 acted well in response to the magnetic field, and its purity was further increased to 95-96% through a two-stage process consisting of grinding and magnetic separation. The nano-sized spherical NiFe 2 O 4 with saturation magnetization of 34.66 and 30.82 emu. g -1 was obtained from H 2 SO 4 and HCl residues, respectively. The dissolved vanadium was recovered as V 2 O 5 via oxidation-precipitation in sulfate media and oxidation-ammonium precipitation in chloride solution. The purity of V 2 O 5 in sulfate and chloride media was 93% and 98.5%, respectively. Finally, a life cycle assessment (LCA) study was performed on the suggested methods to track the ecological effects of extracting V and Ni from oil combustion FA. According to the performed LCA, H 2 SO 4 was determined as the proper leaching reagent considering the environmental and technical aspects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In this paper, a new framework is developed for evaluating the resilience of urban drainage systems (UDSs) under floods by proposing and quantifying some technical and socio-ecological (SE) criteria. The proposed criteria are used to quantify the seven principles of building resilience in socio-ecological systems. The criteria mainly focus on preserving diversity and multiplicity in a UDS, managing variables that gradually change over time (slow variables), improving structural and functional connectivity, maintaining system adaptability, encouraging learning, broadening participation, and promoting polycentric governance systems. For evaluating the efficiency of the proposed framework, it is applied to a real-world case study of improving resilience of the UDS in the eastern part of Tehran metropolitan area. Three scenarios for flood management are proposed based on the Low Impact Development (LID) practices which are simulated using the Storm Water Management Model (SWMM). The Entropy method is used to consider the uncertainty in the relative importance of different criteria in estimating the flood resilience. The estimated values for the proposed criteria regarding the current drainage system in the study area show its undesirable condition in many sub-catchments. The results also show that using around 2.3 km 2 of LID practices in this urban watershed can significantly improve the resilience in many sub-catchments (nearly, 30%) and reduce the total volume of the overflow (about 50%). The results also show that using the flood management scenarios, improving connectivity is the most influential factor that enhances the general resilience of the system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Crimean-Congo haemorrhagic fever (CCHF) is a re-emerging viral haemorrhagic fever causing outbreaks in Iran in the last 15 years. In this systematic review and meta-analysis, the status of Crimean-Congo haemorrhagic fever virus (CCHFV) in ticks would be evaluated. PubMed, Google Scholar and Web of Science were searched for peer-reviewed original papers published between 2000 and 1 July 2022. We included papers that evaluated the prevalence of CCHFV in individual ticks using reverse transcription polymerase chain reaction (RT-PCR). The pooled prevalence of CCHFV was 6.0% (95% confidence interval [CI]: 4.5-7.9%), with heterogeneity (I 2  = 82.706; P < 0.0001). The prevalence of CCHFV was higher related to regions with above sea level of 1001-1500m (6.4%; 95% CI: 4.3-9.5%), an average temperature of ≤15 °C (8.3%; 95% CI: 5.6-12.0%), latitude of ≥36° (8.1%; 95% CI: 5.2-12.3%), an annual rainfall of 101-300 mm (9.8%; 95% CI: 6.1-15.4%) and humidity of ≥61% (10.2%; 95% CI: 5.1-19.3%). Due to the importance of CCHF, it is better to do new epidemiologic studies on ticks by related organizations and adjacent regions of some provinces in which human cases have been previously reported., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The gypsum habitats of Iran are significant reserves of biodiversity containing endemic and rare species. Despite the limited understanding of its characteristics and habitat, it has become essential to study the endemic species of the Semnan gypsic soil. Fresh samples of studied species including Acantholimon cymosum, Astragalus fridae, Astragalus semnanensis, Euphorbia gypsicola, Gypsophila mucronofolia, Moltkia gypsaceae and Nepeta eremokosmus were collected in the wild during the growth season. Leaf surface and leaf cross-sections were considered. The longest hair length was related to A. fridae, A. semnanensis and M. gypsaceae species. The shorter hairs belong to the species A. cymosum, G. mucronofolia and E. gypsicola. Crystals called cystolites were seen in the epidermal cell wall of A. semnanensis leaves. The anatomical characteristics of these species' leaves indicate the presence of dry structures. Using micromorphological studies, we analyzed the hairs of the studied species in terms of their shapes, sizes, and densities. We found some species have hairs with special appendages, which is due to the special conditions in which they have grown. Xeromorphic stomata were found on both leaf surfaces of all endemic gypsophyte plants of Semnan. Several studies have shown that gypsophytes have a specialized mechanism for regulating the absorption of sulfur and calcium from soils containing calcium sulfate or gypsum by their roots. The current study provides novel insights into the response of plant species to extreme conditions and potential adaptation strategies at micromorphological levels., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based simulation-optimization framework (EPANET-NSGA-III) combined with a decision support model (GMCR) is proposed in this study to determine optimal locations for contaminant flushing hydrants under an array of potentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to minimize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compromise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved. A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly 80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization problems. The framework's capacity to address real-world problems was evaluated for the WDS operating in Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting a single flushing strategy, which not only optimally reduced risks associated with contamination events, but provided acceptable coverage against such threats, flushing 35-61.3% of input contamination mass on average, and reducing average time-to-return to normal conditions by 14.4-60.2%, while employing less than half of the initial potential hydrants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The soil pollution status, levels of exposure, and potential ecological and health risks to workers and residents by potentially harmful elements (PHEs) in the Angouran mining area (Iran), the biggest Zn-Pb mine in the Middle East, were studied. To this aim, topsoil (0-5 cm) samples (n = 63) from different land-uses were analyzed for their total PHEs concentrations. Mine worker's blood analysis and in vitro digestion extractions were applied in conjunction with human health risk assessment (HHRA) to assess the potential health impacts by exposure to PHEs. The maximum PHEs total concentrations were found in the soils near the waste rock dumps. HHRA indicated that ingestion of soils may induce a non-carcinogenic risk due to As and Pb (for both age groups of children and adults), while dermal contact for children may induce the same type of risk due to Cd, and Pb. The carcinogenic risks (CR s ) of As, Cd, Cr, and Ni through ingestion route were above the acceptable value of 1 × 10 -4 , and children may face greater health risks. The average blood Zn, Pb, and Cd levels in the mine workers largely exceeded the safe concentration for adults, while 30% of the workers were tested positive for As in blood. In vitro digestion extractions indicated that the highest bioaccessible contents of As, Cd, Pb, Ni and Zn were found for the industrial-residential and mine soils in the area, while those of Cr and Cu were observed in the agricultural use soils. This study illustrates that a combination of techniques, including geochemical analysis, in vitro bioaccessibility extractions, HHRA, and blood analysis, is a workable integrated approach for evaluating pollution and health risks in mining districts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

In this study, numerical groundwater modelling software (GMS) was applied for a 2D transient state predictive (flow and contaminant fate and transport) conceptual model for heavy metal (Selenium in this research) contaminated groundwater, Imamzadeh-Jafar Aquifer, Kohgiluyeh and Boyer-Ahmad Province, Iran. The performances of permeable reactive barrier (PRB) in pollutant removal in the contaminated aquifers were studied by helping the MODFLOW-MT3DMS model. The spatiotemporal distribution of Selenium (Se) contaminant over the aquifer was illustrated using the calibrated flow and contaminant model. According to the findings, the downward movement of Se has resulted in an unsafe and undesirable water quality status in the Imamzadeh-Jafar aquifer, which is supported by field data. The sensitivity analysis of PRB layouts, geometric features, and reactant material characteristics was conducted in groundwater remediation. The numerical model results illustrated that the PRB thickness, ranging from 10 to 500 m, manifested the drop in Se concentration approximately from 40 to 46%. The results shed light on the hydraulic conductivity variations of reactant materials have effects less than 0.5% in Se removals. Furthermore, the decay rate variations in the ranges from 0.0001 to 0.01 d -1 could result in Se removal from 5 to 100%. According to studies, if the contaminant sources are prevented, in a) installation of PRB and b) not installation of PRB scenarios, the Imamzadeh-Jafar aquifer remediation will take 6 months and 84 months, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Modeling Water-Energy-Food (WEF) nexus is necessary for integrated water resources management (IWRM), especially in urban areas. This paper presents a new urban water metabolism-based methodology for WEF nexus modeling and management. A behavioral simulation model is used to incorporate the characteristics of stakeholders in an urban area. Modified versions of the Borda count, Copeland rule, and fallback bargaining procedures are implemented to choose the socially acceptable management scenarios. Finally, the selected scenarios' effectiveness is evaluated using the fairness and total utility indices. The applicability of the proposed methodology is evaluated by applying it to the Kan River basin, Tehran, Iran, which is suffering from some water and environmental issues. The considered management scenarios include adding new water sources, leakage control plans, using rubber dams for enhancing groundwater recharge, revising water allocation priorities, and developing semi-centralized or decentralized reuse strategies for reclaimed wastewater. Results illustrate that considering different fluxes (i.e., water quantity, pollutants, energy, greenhouse gases (GHG), and materials) is as important as incorporating the social characteristics of stakeholders. Simulating the socially acceptable scenario shows that the aquifer's average water level improves by 3 (m), and its average nitrate concentration reduces by 16 (mg/L) in comparison with the business as usual (BAU) scenario. In addition, by implementing different water reuse strategies, which are energy-intensive, total energy consumption is reduced by 5% due to less groundwater pumping. Also, the selected scenario decreases GHG emissions by 18% and increases the sequestrated carbon dioxide by 20%. In conclusion, the proposed decision support tool can provide policies for sustainable water resources management considering water quality and quantity issues, energy usage, and GHG emission., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Mountains play a key role in forming biodiversity by acting both as barriers to gene flow among populations and as corridors for the migration of populations adapted to the conditions prevailing at high elevations. The Anatolian and the Zagros Mountains are located in the Alpine-Himalayan belt. The formation of these mountains has influenced the distribution and isolation of the animal population since the late Cenozoic. Apathya is a genus of lacertid lizards distributed along these mountains with two species, i.e., Apathya cappadocica and Apathya yassujica. The taxonomy status of lineages within the genus is complicated. In this study, we tried to collect extensive samples from throughout the distribution range, especially within the Zagros Mountains. Also, we used five genetic markers, two mitochondrial (COI and Cyt b) and three nuclear (C-mos, NKTR, and MCIR), to resolve the phylogenetic relationships within the genus and explain several possible scenarios that shaped multiple genetic structures. The combination of results in the current study indicated eight well-support monophyletic lineages that separated to two main groups; group 1 including A. c. cappadocica, A. c. muhtari and A. c. wolteri, group 2 contains four regional clades Turkey, Urmia, Baneh and Ilam, and finally a single clade belonging to the species A. yassujica. In contrast to previous studies, Apathya cappadocica urmiana was divided into four clades and three clades were recognized within Iranian boundaries. The clades have dispersed from Anatolia to adjacent regions in the south of Anatolia and the western Zagros Mountains. According to the evidence generated in this study this clade is paraphyletic. Based on our assumption, orogeny activities and also climate fluctuations in Middle Miocene and Pleistocene have influenced to formation of lineages. In this study we revisit the taxonomy of the genus and demonstrate that the species diversity was substantially underestimated. Our findings suggest that each of the eight clades corresponding to subspecies and distinct geographic regions deserve to be promoted to species level., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Due to land-use and hydrology changes, people are constantly exposed to floods. The adverse impact of floods is greater on vulnerable populations that disproportionately inhabit flood-prone areas. This paper reports a comprehensive study on flood vulnerability of flood prone areas in residential areas of the Tajan watershed, Iran in two periods before 2006 and after 2006. Flood prone area were determined by the random forest (RF) and K-nearest neighbor (KNN) machine learning methods. To reduce time and cost, the vulnerability was assessed only in areas with very high flood hazard using 4 main criteria (social, policy, economic, infrastructure), 40 items, and 210 questionnaires across 40 villages. Independent t-test, Kruskal-Wallis, and paired t-test were used for statistical analysis of questionnaire data. The results of machine learning models (MLMs) showed that the RF model with AUC = 0.92% is more accurate in determining flood prone areas. The results of paired t-test showed that the three criteria of social (mean P1 = 2.97 and P2 = 3.35), infrastructure (mean P1 = 2.88 and P2 = 3.25), and policy (mean P1 = 3.02 and P2 = 3.50) had significant changes in both periods. The Kruskal-Wallis test also revealed the mean of all four criteria in both periods and all sub-watersheds, except three sub-watersheds 10 (Khalkhil village), 19 (Tellarem and Kerasp villages), and 23 (Dinehsar and Jafarabad), had a significant difference. The results of the t-test also showed a decrease in vulnerability in the second period (before 2006) compared to the first period (after 2006), so the number of sub-watersheds in the very high vulnerability class was more in the first period than in the second period. A vulnerability map was developed using three factors of risk zone area, area of each sub-watershed, and population of each sub-watershed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Distributed Constraint Optimization (DCOP)-based approaches, as the distributed version of constraint optimization, provide a framework for coordinated decision making by a team of agents. In this paper, an agent-based DCOP model is developed to allocate water and reclaimed wastewater to demands considering the conflicting interests of involved stakeholders. One of the well-known DCOP algorithms, ADOPT 1 , is modified to incorporate an agent responsible for monitoring and conserving water resources. This new algorithm considers the social characteristics of agents and a new form of interaction between agents. For the first time in the literature, a real-world water and reclaimed wastewater allocation problem is formulated as a DCOP and solved using the Modified ADOPT (MADOPT) algorithm. To evaluate the MADOPT algorithm, it is applied to a water and reclaimed wastewater allocation problem in Tehran, Iran. The results illustrate the applicability and efficiency of the proposed methodology in dealing with large-scale multi-agent water resources systems. It is also shown that agents' selfishness and social relationships could affect their water use policies., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The present study aimed to evaluate the psychometric properties of a Persian version of the Psychological Flexibility Epilepsy Questionnaire (P-PFEQ). Transcultural adaptation and validation of the Persian version of the PFEQ were performed using translation and back-translation with pilot testing (on 17 patients) and expert evaluation. Participants in the current study involved 100 patients with an EEG-verified epilepsy diagnosis and an average age of 30.96 years (SD ± 6.46) (63% were female). Data collection included a sociodemographic questionnaire, epilepsy-specific questionnaire, the Depression-Anxiety-stress scale (DASS-21), the Insomnia Severity Index (ISI), the 31-item Quality-of-Life questionnaire in Epilepsy (QOLIE-31), the Acceptance and Action Questionnaire (AAQ), the Committed Action Questionnaire (CAQ), and the short form of the Freiburg Mindfulness Inventory (FMI). Descriptive and inferential analyses were conducted by SPSS software V.26. The P-PFEQ demonstrated semantic, conceptual, and content validity in equivalence with the Swedish version. Based on Exploratory Factor Analysis (EFA), five items were eliminated and unidimensional scale of PFEQ with 12 items, explaining 50.38% of the total variance, was accepted. The scale showed good reliability through internal consistency (Cronbach's α of 0.9) and temporal stability on retest (n = 85, Intraclass correlation = 0.92). Convergent and divergent validity findings were acceptable for the P-PFEQ. The findings show that the P-PFEQ is a reliable and valid scale for assessing psychological flexibility in the Iranian patients with epilepsy. Also, it can be confidently recommended as a useful instrument in clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background and Purpose: Seizure severity has been increasingly gaining attention as a complementary assessment to seizure frequency for the measurement of treatment responses. This study aimed to assess the reliability and external validity and of the Persian version of the Seizure Severity Questionnaire (SSQ)., Methods: The study sample was recruited from 126 patients with epilepsy who attended the neurology outpatient clinic at Imam Khomeini and Roozbeh hospitals, Tehran, Iran. The Forward-Backward technique was applied to translate the questionnaire. The reliability of SSQ was assessed by Cronbach's alpha coefficient. The external validity of SSQ was assessed by correlating SSQ scores with Quality of Life in Epilepsy Inventory-31 (QOLIE-31) subscales., Results: The sample comprised 63 women (50%) and 63 men (50%) aged 13-76 years. The mean scores of SSQ items ranged from 3.46 to 5.48. Distribution was skewed for all component scores, with a tendency for the item scores to concentrate toward the highest scores. Reliability for almost all domains were moderate to good, with Cronbach's alpha ranging from 0.615 to 0.770. Component B to D and total score of SSQ had weak-to-moderate inverse correlation with QOLIE-31 subscale scores. However, the result showed no significant correlation with age, sex, or education., Conclusion: With some limitations, the Persian version of the SSQ shows relatively good reliability and content validity, supporting its use as a specific measure of seizure severity in epilepsy in Iran., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)

While being affected by economic and hydrological conditions, the behaviors of water market participants can also be caused by their psychological characteristics and social stimuli of the environment. This paper employs agent-based modeling (ABM) approach to simulate a local groundwater market in central Iran. The proposed ABM framework couples social, economic, and hydrological sub-models. The social sub-model benefits from the theory of planned behavior under field studies to design psychology-based behaviors of trading agents. Moreover, in continuous interaction with the FlowLogo hydrological sub-model, the economic sub-model simulates the inter-sectoral water trading under a double-auction mechanism. The inter-sectoral trading includes selling the farms' irrigation water to the industry sector. The calibration and validation results for an eight-year simulation period (2010-2018) confirm the acceptable performance of the proposed ABM framework. Water trading patterns experience relatively extreme variations in the first years. However, with the adaption of the agents' bids to the market conditions, they gradually emerge in a more stable form in the last years. Furthermore, updating the psychological factors increases the agents' intention of participating in the market, and thus, the competition level over time. Finally, the hydro-economic analysis reveals that implementing the dynamic cap-and-trade policy increases the total net benefits of market participants by an average of 27% per year while reducing the region's groundwater drawdown by 56 cm. Such inter-sectoral water markets can help with the sustainable exploitation of groundwater resources., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1: maps SVI; Module 2: develops a predictive model for Groundwater Levels (GWL); Module 3: combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1, multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2, FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain., (Copyright © 2021 Elsevier Ltd. All rights reserved.)