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Background: Health policy and systems research (HPSR) can strengthen health systems and improve population health outcomes. In the Eastern Mediterranean Region (EMR), there is limited recognition of the importance of HPSR and funding remains the main challenge. This study seeks to: (1) assess the reporting of funding in HPSR papers published between 2010 and 2022 in the EMR, (2) examine the source of funding in the published HPSR papers in the EMR and (3) explore variables influencing funding sources, including any difference in funding sources for coronavirus disease 2019 (COVID-19)-related articles. Methods: We conducted a rapid scoping review of HPSR papers published between 2010 and 2022 (inclusively) in the EMR, addressing the following areas: reporting of funding in HPSR papers, source of funding in the published HPSR papers, authors' affiliations and country of focus. We followed the Joanna Briggs Institute (JBI) guidelines for conducting scoping reviews. We also conducted univariate and bivariate analyses for all variables at 0.05 significance level. Results: Of 10,797 articles screened, 3408 were included (of which 9.3% were COVID-19-related). More than half of the included articles originated from three EMR countries: Iran (n = 1018, 29.9%), the Kingdom of Saudi Arabia (n = 595, 17.5%) and Pakistan (n = 360, 10.6%). Approximately 30% of the included articles did not report any details on study funding. Among articles that reported funding (n = 1346, 39.5%), analysis of funding sources across all country income groups revealed that the most prominent source was national (55.4%), followed by international (41.7%) and lastly regional sources (3%). Among the national funding sources, universities accounted for 76.8%, while governments accounted for 14.9%. Further analysis of funding sources by country income group showed that, in low-income and lower-middle-income countries, all or the majority of funding came from international sources, while in high-income and upper-middle-income countries, national funding sources, mainly universities, were the primary sources of funding. The majority of funded articles' first authors were affiliated with academia/university, while a minority were affiliated with government, healthcare organizations or intergovernmental organizations. We identified the following characteristics to be significantly associated with the funding source: country income level, the focus of HPSR articles (within the EMR only, or extending beyond the EMR as part of international research consortia), and the first author's affiliation. Similar funding patterns were observed for COVID-19-related HPSR articles, with national funding sources (78.95%), mainly universities, comprising the main source of funding. In contrast, international funding sources decreased to 15.8%. Conclusion: This is the first study to address the reporting of funding and funding sources in published HPSR articles in the EMR. Approximately 30% of HPSR articles did not report on the funding source. Study findings revealed heavy reliance on universities and international funding sources with minimal role of national governments and regional entities in funding HPSR articles in the EMR. We provide implications for policy and practice to enhance the profile of HPSR in the region. [ABSTRACT FROM AUTHOR]

Noise pollution is one of the consequences of urbanization that can cause environmental disturbances in urban areas. Urban ecosystems provide noise reduction services through Urban Green infrastructures (UGIs). Many studies have been conducted to evaluate and model traffic noise, but none have addressed the flow, supply, and demand of noise reduction ecosystem services. The main purpose of this paper is to present a new methodology for estimating flow, supply, and demand for noise reduction in Hamadan city that has not been mentioned in any paper so far. UGIs were classified into six main categories: agricultural lands, gardens, parks, abandoned lands, single trees, and street trees. A total of 57 sampling stations for sound measurement were made in August 2018. The current map of noise pollution (flow) was created using the Kriging method. The amount of supply was measured up to a distance of 50 meters from the main roads based on two approaches (the distance effect and the sound barrier effect). To quantify the demand, the current sound intensity level in the noise-sensitive land uses was compared with standards. Zonal statistics was used for spatial analysis of supply-demand in the urban neighborhood as a working unit. Results showed that at distances of 5m, 10m, 15m, and 20m, the average noise reduction was found to be 1.61, 2.83, 3.92, and 5.33 dB, respectively. Sound barriers at distances of 5m and 10m resulted in an average sound reduction of 1.61 and 2.83 dB, respectively. Individual trees, strip trees, abandoned lands, parks, and gardens led to a decrease in traffic noise by 0.3, 1, 0.1, 3.5, and 4.5 dB, respectively. The clustering analysis revealed a significant spatial clustering of noise pollution in Hamedan. The results and new methodology of this research can be used in similar areas to estimate the supply and demand of noise reduction and also for decision-makers to take management actions to increase supply and meet the demand for noise reduction service. [ABSTRACT FROM AUTHOR]

Background: Hitherto, no instrumental research has been designed to measure the addiction potential for children in Iran. Therefore, the questionnaires designed to investigate addiction were examined in this systematic review article. Methods: In the present systematic review, all Iranian and foreign research on addiction potential for children were examined until March 2022. Using separate keywords and their Latin synonyms according to Mesh terms, an extensive search was conducted in databases: PubMed, Magiran, Iranmedex, Medline, Google Scholar, SID, and Irandoc. In total, 173 Farsi and English papers were included in the initial list, and after evaluation according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist and acceptance criteria, 43 final papers were selected for systematic review. Findings: Through searching in Persian and English databases and using related keywords, 173 papers were found in the initial search and after removing duplicates and unrelated studies, 43 papers were selected for systematic review. In this research, papers related to the addiction potential between 1998 and 2019 were considered, and among these, 22 papers particularly dealt with addiction. In the conducted studies, the awareness, attitude, talent, and factors affecting addiction were mostly measured and in some papers, addiction potential was measured. Conclusion: Designing psychometrically appropriate tools is necessary to measure the addiction potential of children in Iran. Due to the unavailability of appropriate tools and the fact that the previous versions are not up to date and are not checked regularly, for better and more complete decision-making in the health policy-making process more investigation in this field is necessary. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

Bringing rightist movement studies into the Iranian context, this study advances a generalizable understanding of the ideological, moral, and cultural activism of Islamist movements and their rightist counterparts. While numerous studies have discussed the economic explanation of rightist movements, I integrate Islamist movements in the Muslim world and rightist movements in the West to develop a generalizable cultural and moral explanation of rightist movements. Value and ideological conflicts, as well as moral outrage, drive this integrated understanding of rightist movements. The rise of innovative and contentious forms of millennialism in Iran—especially the increasing salience of the Jamkaran mosque, the rise of new media outlets and millennial discourses, and pertinent policies—provide evidence for proposing this generalizable understanding. I argue that the rise of performative contentions surrounding millennialism, known as Mahdaviat, within the pro-regime revolutionary rightist movement in Iran was Islamists' ideological response to liberal threat perceptions. These threat perceptions were activated before the liberal Reform era (1997–2005). After the ascent of Ahmadinejad to power in 2005, ideological millennialism became the dominant discursive field in Iran's state politics. Drawing on narratives of prominent Islamist figures and media personalities in Iran and events surrounding Mahdaviat, this paper advances a generalizable argument of the moral and cultural explanation of rightist movements. [ABSTRACT FROM AUTHOR]

In the restoration or rehabilitation of traditional buildings, compatible materials with known characteristics must be used. However, the existing literature lacks comprehensive studies on the characterisation of Persian mud–straw plaster, focusing primarily on Persian adobe. Moreover, previous research on Persian adobe has primarily employed XRF and XRD tests, neglecting ion chromatography, moisture sorption isotherm determination, and thermogravimetric analysis with differential scanning calorimetry. Consequently, there is a shortage of information regarding the elemental composition, mineralogical characteristics, moisture sorption behaviour, and thermal properties of Persian mud–straw plaster, as well as Persian adobe bricks. This paper aims to address this research gap by examining historical and new adobe bricks and mud–straw plaster used in Iran, utilising a comprehensive array of analytical techniques. The results from XRF analysis reveal relatively similar chemical compositions across all samples, while XRD analysis indicates predominantly similar mineral phases. Ion chromatography results demonstrate higher conductivity and chloride concentrations in the mud–straw samples than the adobe samples, with higher values for new samples than historical ones. Freshly used straw, clay, or soil may have higher chloride concentrations caused by the arid climate and soil salinisation in the area. Additionally, moisture sorption isotherm determination results show that adobe and mud–straw plaster with a higher salt load of chlorides have significantly higher moisture absorption. The increased straw quantity in the samples increases the moisture content. Furthermore, thermogravimetric analysis and differential scanning calorimetry indicate that, at low heating, adobe and mud–straw plaster lose water due to dehydration, and at high heating, they lose carbon dioxide due to decarboxylation. The comprehensive characterisation of Persian adobe and mud–straw plaster in this study fills a significant gap in the literature and offers invaluable insights for informing restoration and rehabilitation processes, ensuring the compatibility of the materials used. [ABSTRACT FROM AUTHOR]

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to evaluate the preparedness of Iranian nurses for potential pandemics. Background: Nurses play a critical role in managing pandemics. They require adequate training, proper equipment, and organizational support to be well-prepared. Methods: A descriptive cross-sectional study was conducted in Ardabil, Iran, from July to September 2023, involving 233 nurses from five hospitals. The number of nurses required for each hospital was calculated based on the proportion of nurses in each hospital. Data was collected through a paper-based form that included information about the participants' demographic characteristics and their level of pandemic preparedness in health services. The collected data was analyzed using descriptive statistics to determine the demographic characteristics and levels of pandemic preparedness. Pearson's test was also conducted to establish a relationship between different dimensions of pandemic preparedness. Results: Most participants relied on clinical measures and supported using human resources and environmental methods to curb the transmission of a pandemic. They felt assured in their ability to explain the preventive measures against the pandemic. However, fewer respondents had access to healthcare improvement programs, and only a few worked remotely from home. Conclusions: According to our study, 90.1% of nurses believe hand washing is the most effective way to prevent spreading infections. Additionally, healthcare professionals can use various tools to respond to the pandemic, including screening for COVID-19 at work, health and wellness programs, telecommuting, COVID-19 Safe programs, social media, and posters. Nurses need continuous education in hand hygiene, health programs, remote work options, and pandemic-safe programs to control infections, reduce risks, and optimize patient care during the pandemic. [ABSTRACT FROM AUTHOR]

Aims: The purpose of this study was to evaluate the preparedness of Iranian nurses for potential pandemics. Background: Nurses play a critical role in managing pandemics. They require adequate training, proper equipment, and organizational support to be well-prepared. Methods: A descriptive cross-sectional study was conducted in Ardabil, Iran, from July to September 2023, involving 233 nurses from five hospitals. The number of nurses required for each hospital was calculated based on the proportion of nurses in each hospital. Data was collected through a paper-based form that included information about the participants' demographic characteristics and their level of pandemic preparedness in health services. The collected data was analyzed using descriptive statistics to determine the demographic characteristics and levels of pandemic preparedness. Pearson's test was also conducted to establish a relationship between different dimensions of pandemic preparedness. Results: Most participants relied on clinical measures and supported using human resources and environmental methods to curb the transmission of a pandemic. They felt assured in their ability to explain the preventive measures against the pandemic. However, fewer respondents had access to healthcare improvement programs, and only a few worked remotely from home. Conclusions: According to our study, 90.1% of nurses believe hand washing is the most effective way to prevent spreading infections. Additionally, healthcare professionals can use various tools to respond to the pandemic, including screening for COVID-19 at work, health and wellness programs, telecommuting, COVID-19 Safe programs, social media, and posters. Nurses need continuous education in hand hygiene, health programs, remote work options, and pandemic-safe programs to control infections, reduce risks, and optimize patient care during the pandemic. [ABSTRACT FROM AUTHOR]

Background: Zoonotic cutaneous leishmaniasis (ZCL) is widely distributed in Iran and around the world. Also, Khuzestan Province is an endemic focus of ZCL. This study aims to investigate the natural infection of sand flies with the Leishmania parasite in Karun County. Methods: Sand flies were collected from Jangiyeh, Qaleh Chanan, Kut-e-Navaser, and Ghazavieh in the spring and summer in the year of 2019, by installing 60 sticky paper traps each time (30 traps outdoors and 30 traps indoors). Two hundred female sand flies with different abdominal conditions (empty, blood-fed, semi-gravid, and gravid) were examined for infection rate using the Nested-PCR method. Results: In this study, seven species of sand flies including Phlebotomus papatasi, Ph. alexandri, Ph. sergenti, Ph. caucasicus, Sergentomyia tiberiadis, Se. sintoni, and Se. antennata were reported from Karun County, with a frequency of 79.64%, 16.96%, 1.07%, 0.18%, 0.36%, 1.61%, and 0.18%, respectively. Only eleven specimens of Ph. papatasi were found to be positive for Leishmania major, with an overall infection rate of 7.8%. The infection of Ph. papatasi was specifically reported in blood-fed, gravid, and semi-gravid specimens, with infection rates of 17.02%, 4.35%, and 14.29%, respectively. Conclusion: In this study, the infection of L. major from Ph. papatasi was reported. The results can be used in planning the control of ZCL in the study area. [ABSTRACT FROM AUTHOR]

The article is about private law contracts and their social, economic, and religious background in Sasanian Persia (224–651) before the advent of Islam. After a brief introduction to the sources and research method, the paper examines Zoroastrian moral teachings about obligations in general and contractual obligations in particular. To emphasise the religious background, the Avestan hymn to Mithra and the Vendidad are scrutinised which have a classification of contracts and a pyramid of sanctions for their breach. The second part of the article analyses private law contracts, which can be found in the Sasanian Law Book called Madigan I Hazar Dadestan, such as sale, barter, donation, lease, and loan, together with legal guarantees such as surety and pledge. In addition to legal dogmatics, attention is also paid to gaps in the law and the opportunities they provide for parties to achieve unlawful ends with legal means, as well as the prudence of Sasanian lawyers and how they stopped such abuses of the law. [ABSTRACT FROM AUTHOR]

Over the past two decades, human geneticists have substantially embraced the concept of "biogeographical ancestry" to account for the racial, ethnic, and linguistic categories they use to analyze and interpret genetic difference. Understanding the ongoing role of these categories in human genetic research therefore requires attention to geneticists' representations of geography, particularly the geographic maps they use to illustrate gene distribution and migration. This article examines how the methods and imagery of international genetic geography and its major evolutionary narratives have reinforced or refashioned nationalist practices of geography in the Middle East. Geneticists simultaneously conceptualize the region's physical space as both a historical "crossroads" of human migration and the birthplace of distinct gene sequences and civilizations, alternately blurring and sharpening the boundaries between Europe and Asia. Focusing on genetic research in Turkey and Iran, this paper analyzes how geneticists draw and interpret geographic maps of the region while selectively erasing or highlighting state borders. These genetic maps negotiate between the idealized aims of international projects to reconstruct human evolutionary history, and the reality of practicing science under the constraints of nationstate politics. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing (HF) is the primary choice for stimulating petroleum reservoirs. Rock tensile strength and brittleness are crucial parameters required for screening candidate reservoirs and in designing successful HF operations. However, in situ and laboratory determinations of the hydraulic tensile strength (HTS) of rock can prove problematic. Alternatively, the HTS could be estimated from the rock brittleness once a reliable relationship has been established between them. Accordingly, this paper investigates the correlations between the HTS, as measured using laboratory hydraulic fracture tests, and ten strength-based brittleness indices (BIs) selected from the research literature. The primary inputs for computing these BIs are uniaxial compressive strength (UCS) and the Brazilian tensile strength (BTS), which are typically measured for most projects using standard laboratory rock mechanics tests or obtained from log data. For the purposes of this experimental investigation, intact rock core samples were obtained from a carbonate–dolomite formation in Iran, comprising eight distinct geomechanical units, with measured values of UCS, BTS, and HTS ranging 29.7–162.2, 1.93–12.23, and 7.20–20.63 MPa, respectively. The measured HTS was found to directly correlate with the UCS, BTS, and Young's modulus, and inversely correlated with the rock porosity. Seven of the ten investigated BIs correlated with the measured HTS over 69% (R2 ≥ 0.69). In particular, the BI expressions developed by Yagiz and Gokceoglu, Ghadernejad et al., and Khandelwal et al. exhibited relatively strong correlations with the measured HTS (producing R2 values of 0.94, 0.87, and 0.86, respectively), suggesting that these three HTS–BI correlations could be used to provide preliminary HTS estimates for the investigated carbonate–dolomite formation in Iran. This work adds to a database that can be expanded to include other geographical regions for providing useful information about the selection of a suitable site or reserve for conducting HF operations. [ABSTRACT FROM AUTHOR]

Damsel bugs (Hemiptera, Nabidae) are predatory insects belonging to the order of Hemiptera. During the spring-summer of 2021-2022, nabid specimens were collected from Guilan province and adjacent areas. In this study, three species (four subspecies) belonging to two genera were collected and identified: Himacerus mirmicoides (O.Costa, 1834); Nabis viridulus Spinola, 1837; Nabis pseudoferus pseudoferus Remane, 1949 and Nabis pseudoferus orientarius Remane, 1962, furthermore, four species (four subspecies) belonging to two genera were examined from the insect collection of the Natural History Museum of the University of Guilan (UGNHM) including Himacerus major (A.Costa, 1842); Nabis pseudoferus transcaspicus Remane, 1962; Nabis punctatus punctatus A.Costa, 1847 and Nabis capsiformis Germar, 1838. In this paper, based on the morphological characteristics and genitalia of males, brief diagnostic descriptions, illustrated identification key and digital images of species habitus were provided to facilitate the identification of Nabidae of Guilan province and its adjacent areas. [ABSTRACT FROM AUTHOR]

Rabies is a prevalent endemic and zoonotic fatal disease, which is normally transmitted to humans by contact (scratches and bites) from infected animals. The present paper deals with the first documented evidence of rabies in the stone marten (Martes foina). Rabies symptoms were observed in a marten in Chaharmahal and Bakhtiari province. The animal with a strange demeanour approached people without fear, which was died after some while. Samples were taken from its brain (cerebellum, hippocampus, and hypothalamus), shortly after death. In this report, laboratory evidence of rabies by fluorescent antibody (FAT) was proved. The present work is valuable because of the environmental importance of the stone marten. Hence, sensitive surveillance and advanced reporting systems should be regularly monitored on suspected cases of rabies in animals and humans to control and prevent this deadly disease. This involves exposure history, clinical examinations, symptoms and experimental results. Rabies can be controlled by fast diagnostic tests and vaccination. [ABSTRACT FROM AUTHOR]

This paper presents an online scheme of a wide-area-damping-controller (WADC) for controlling the inter-area oscillatory modes in interconnected power systems using wide-area power system stabilizers and measuring signals. The proposed adaptive controller includes two individual approaches WSSS for evaluating the signal participation factor selection and GPSS as a global power system stabilizer for damping inter-area oscillations. For this purpose, considering the concept of center of inertia over inter-area signals, generators with the highest interactions are determined as oscillating areas while based on evaluating signal damping ratio, proper participation factors are estimated in which GPSS with positive damping performance is developed. In real-time working mode, based on oscillatory signals gathered from WAMS data, different combinations of PSSs are evaluated in which the combinations with the best controllability on damping the oscillations are selected. The effectiveness of the proposed widearea damping controller scheme is evaluated on the IEEE test system and practical Iran national power grid with the potential of unstable inter-area oscillations. Results show the effectiveness of the proposed WADC approach for fast and secure damping of inter-area oscillation. [ABSTRACT FROM AUTHOR]

This study explored its research trends by examining Iran's methods, contexts, and theories of information behavior research. This study is applied research regarding objectives and a content analysis regarding methodology. The research population consisted of 325 studies on information behavior. A researcher-made checklist was used to collect data, the validity of which was confirmed by experts. According to the researcher-made checklist, 325 data were analyzed based on the variables of each section. Data analysis was performed using descriptive statistics and SPSS software (version 22). The results indicated the dominance of the quantitative approach and, consequently, the application of statistical analysis tools and methods related to this approach in relevant studies. In addition, most investigations were practical in this field, and therefore, fewer theoretical-philosophical studies and, in other words, fundamental research have been addressed in this field. There is no diversity in studies methodologically, and most of the reviewed studies, while referring to influential research in this field in their backgrounds, do not pay attention to their methodologies. Most of the research works are copies of previous studies conducted using quantitative, applied, and survey methods, and the desired population has changed only in some cases. Moreover, the scope of disciplines and specialties involved in the research production and studies reflects the interdisciplinary nature of this field with emphasis on knowledge and information science. The approach of information behavior research is expanding in various contexts with the presence of different types of users. However, it seems that the information behavior of the general public is less considered, and new studies are required in this regard. Research and studies conducted on information behavior in Iran have mainly considered the international models in this field, and some studies have even attempted to modify them. Few researchers have also proposed new models in this field, some of which have been published in international papers. [ABSTRACT FROM AUTHOR]

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices. • A novel machine learning (KELM-INFO) approach for forecasting TDS is created. • The KELM-INFO is validated by four well-known machine learning methods. • The V-KELM-INFO model is assessed against the metaheuristic-based V-KELM models. • This work provides insight into improving the accuracy of model-based methods. [ABSTRACT FROM AUTHOR]

This research aims to improve the traditional statistical approach to evaluate the seismic resilience of concrete structures exposed to chloride attack. In the mentioned approach, the uncertain parameters are modeled using certain probability distribution functions (PDFs) selected from some standards such as fib. However, the mentioned PDFs should contain the actual conditions of the desired structure. This paper introduces a Bayesian framework for updating PDFs used in deterioration analysis, which is an important part of the life-cycle seismic resilience assessment process of corroded concrete structures. The proposed methodology was applied to a two-story reinforced concrete school in Iran that suffered from chloride corrosion for twenty years. The seismic resilience of the structure was investigated for a 50-year life cycle. The results showed that the updated model can significantly reduce the uncertainty of the influencing parameters in the seismic resilience evaluation process. Using the updated model resulted in a conservative seismic resilience index compared to the non-updated model. This research shows that the role of inspection data in periodically updating the seismic-resistance calculations of corroded concrete structures is not only necessary but also possible and beneficial. • A new methodology for assessing seismic resilience of corroded reinforced concrete systems. • The proposed algorithm integrates the structural inspection data with the seismic resistance assessment process. • The uncertainty of results is reduced using Bayesian framework. • The efficiency of the methodology is evaluated using a real-world case study. [ABSTRACT FROM AUTHOR]

Water scarcity threatens food security and leads to various economic, social, and environmental challenges, particularly in arid and semi-arid regions. As the agricultural sector consumes the most significant amount of water, conserving water resources in this sector is particularly important. Cultivation of low-water-demand (LWD) crops with high value-added is one of the effective measures to reduce agricultural water demand and improve farmers' livelihoods. However, changing water-intensive crops to LWD crops (e.g., medicinal plants) is voluntary pro-environmental behavior. Considering the role of psychological theories in understanding and explaining human behaviors, the main purpose of the current paper is to explore the socio-psychological determinants underlying farmers' intention to adopt an LWD cropping pattern using the Health Belief Model (HBM). In this regard, data were collected using face-to-face interviews with 184 farmers living in Zanjan province, Iran to examine their intention to cultivate LWD medicinal plants. Using structural equation modeling for data analysis, the HBM explained 49% of the variance in farmers' behavioral intentions. The structural equation modeling outcomes also revealed that self-efficacy, perceived barriers, cues to action, and perceived benefits are significantly related to farmers' intention to choose medicinal plants. Overall, the findings indicated the effectiveness and applicability of the HBM in illustrating farmers' intention to change their cropping pattern. Applying some strategies such as providing the necessary facilities and training programs for farmers, as well as, advertisements aimed at promoting the cultivation of medicinal plants can significantly contribute to conserving water resources in the study area. • Evaluating farmers' intention to medicinal crops using the Health Belief Model. • The HBM accounted for 49% of the variance of the farmers' behavioral intentions. • Self-efficacy, perceived expectations, and cues to action affect farmers intentions. • Self-efficacy was identified as the main predictor of farmers behavioral intentions. • Cultivating medicinal crops help conserving water resources in the study area. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of reliable and green routing in a road-rail multimodal transportation network. To enhance network reliability, types of disruptions occurring simultaneously in each node and arc are identified. Additionally, carbon emission is considered a risk caused by transportation process disruptions for achieving green routing. A combination of rating methods such as DEMATEL-based Analytic Network Process (DANP) and the Combined Compromise Solution (COCOSO), along with Failure mode and effects analysis (FMEA) Criticality reliability assignment method, is used to evaluate disruptions and select a safe route from origins to destinations. The DANP method produces a ranking of sub-criteria, and the COCOSO method produces a ranking of criteria in the network. The FMEA Criticality reliability assignment method is used to assign reliability to each option. Next, the route with the highest reliability from each origin to each destination is selected. The presented model investigates a cement road-rail transportation network in Iran. After analyzing the sensitivity of criteria and sub-criteria, the results provide best routes with increased reliability and reduced emissions risk. • Identifying risks in each node and arc in the transportation network. • Prioritizing criteria, sub-criteria, and options using a mix of DANP and COCOSO ranking methods. • Assessing the reliability of each option using the FMEA Criticality method. • Selecting the best path with the highest reliability and the less carbon emission risk in each O-D pair. • Considering a case study in the cement road-rail transportation network in Iran. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Bayesian network is benchmarked against eight AI models to predict wastewater quality. • Bayesian networks can handle missing value in input data due to probabilistic nature. • Key variables in WWTP quality and quantity were specified. • Input data quality has a huge role in successful development of data-driven models. • The prediction error sharply increases when the portion of missing data exceeds 10%. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives. • Adaptive reservoir operation model considering water quantity and quality objectives. • Using ECMWF-based rainfall forecasts with monthly time step and 6-month lead time. • Linking the optimization model with the CE-QUAL-W2 water quality simulation model. • Applying the real-time operation model to the Karkheh river-reservoir system in Iran. • Illustrating the efficiency of the proposed model in a real-world large-scale problem. [ABSTRACT FROM AUTHOR]

An Erratum to this paper has been published: https://doi.org/10.1134/S1028334X2401001X [ABSTRACT FROM AUTHOR]

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. • Developing a new framework for coordinated operation of urban detention reservoirs. • Using deep-learning-based rainfall nowcasting data for real-time flood management. • Linking a stormwater simulation model with the cellular automata optimization model. • Applying the framework to the east drainage catchment of the Tehran metropolitan area. • Illustrating that the adaptive framework reduces the overflow volume by up to 60%. [ABSTRACT FROM AUTHOR]

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH. [ABSTRACT FROM AUTHOR]

One of the most important features of a reliable power system is its capability to supply the demand continuously. This continuous supply has been maintained by the transient stability of the system against large disturbances. The study of this type of stability is examined through different indicators. One of the common indicators to evaluate the transient stability of the power system is the well-known Critical Clearing Time (CCT) index. Conventional methods for calculating CCT have presented good accuracy, however, their computational cost is very high which makes them not suitable for real-time applications and real large-scale networks. Considering their ability to feature extraction of big data, deep neural networks can be utilized as reliable tools to cover these deficiencies. In this regard, to cover the shortcomings of the conventional methods, this paper proposes a method based on deep Convolutional Neural Networks (CNN) to estimate the CCT index in real-time power system applications. Moreover, to analyze a realistic case, nonlinear limiters of the excitation systems which have a considerable effect on transient stability index are considered in this study. Thanks to the using of several deep layers and the comprehensive established database, the accuracy of proposed method is appropriately high. Numerical studies on IEEE standard networks as well as a real case in Iran Power Grid (IPG) represents the advantages of the proposed method. • A deep-learning-based convolutional neural network is designed to estimate CCT index of power system. • Nonlinear limiters of the excitation system are taken into account. • A real case study in Iran grid is considered for simulation. • As a result, the transient stability of system is accurately determined in on-line manner. [ABSTRACT FROM AUTHOR]

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized. [Display omitted] • Regional sea level rise is quantified using GCMs under RCP 8.5. • Flooding maps are shown under regional SLR, tides, storm surge, wave setup and run-up. • Extreme total water level is expected to inundate important ports in Persian Gulf coast of Iran by 2050. • Research projects that 513 km of low-lying coastal areas will be inundated due to extreme total water level. [ABSTRACT FROM AUTHOR]

[Display omitted] • Energy-Water-Food security nexus of mung bean production in Iran is analyzed. • Non-renewable cumulative energy demand is estimated at 27.4 MJ/kg of beans. • Total water footprint is estimated at 1.55 m3/kg of beans. • On-farm production concentrates the largest environmental impacts (40% to 96%). • Cooking accumulates over 50% of the carbon footprint. Mung bean is a very important crop in Iran in both socio-economic and nutritional terms. However, although discussions on food and food security increasingly include sustainability issues, there are no precedents in academic literature that analyze in depth the nexus between energy, water use and food security in relation to this crop in Iran from an agri-food system approach. Therefore, our main objective is to assess the energy-water-food security (EWFs) nexus and the environmental impact of mung bean production in Iran from a "cradle to fork" approach using different nutritional units (1 kg of beans, 1 kg of proteins, and 1000 kcal) and load allocation criteria. In addition, an economic analysis of the farms is carried out. The results show that the on-farm production of mung beans is the phase where the largest environmental impacts are concentrated (between 40 % and 96 % of them, including those related to water and energy use), while cooking accounts for more than 50 % of the carbon footprint. The non-renewable cumulative energy demand (NR CED) and total water footprint (TWF) per kilogram of beans ("cradle to fork") is estimated at 27.4 MJ and 1.55 m3 and the farm Net Margin (NM) is estimated at 3,677 USD per ha. The paper discusses whether mung bean is a low-impact option for protein production, especially when compared to animal products and the importance of using different functional units and load allocation criteria to address the issue of EWFs and sustainability. In this regard, further research is needed to improve the environmental efficiency of bean production, which is critical for promoting sustainable diets in line with food security goals. [ABSTRACT FROM AUTHOR]

This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city. [Display omitted] • Potential of municipal solid waste (MSW) gasification for all Iranian cities. • Comparison of using air, steam, and air-steam agents for MSW gasification. • The payback period of air agent MSW gasification is lower than other agents. • Machine Learning used for prediction of the behavior of MSW gasification. [ABSTRACT FROM AUTHOR]