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Due to the numerous uses of honey as a food ingredient and its therapeutic properties, humans consume a lot of it. This study examines the differentiation between honey products derived from various botanical and geographic origins as well as honey products that contain various types of syrup used in honey adulteration. A colorimetric sensor array based on an electronic tongue and multivariate analysis was used to distinguish honey samples from various producing regions in Iran (Chaharmahal and Bakhtiari, Fars, and Bushehr provinces) and distinct botanical sources (Astragalus, Ziziphus, and Polyfloral). With the advantages of being quick, easy, and inexpensive, the colorimetric sensor array was able to discriminate the honey successfully. Using PLS-DA, the honey samples were classified according to botanical origin with 100% and 95% accuracies for training and cross-validation, respectively. Also, according to the PCA score plot, we were able to distinguish between different ratios of adulterated honey and syrup. The present work aims to use a chemometrics-based sensor array as a quick and non-destructive method for accurate honey classification and authentication. • A sensor array to distinguish pure honey from adulterated honey. • A paper microfluidic to discriminate honey samples of different botanical. • Discrimination between heated and unheated honey. • Identification of geographical origin. [ABSTRACT FROM AUTHOR]

Transportation systems are a basis of disability-related activity involvement in society. In Tehran, the capital of Iran, a paratransit system for disabled people has been operating since 2013, but the market share is gradually losing for reasons unknown to practitioners. Although paratransit systems for people with disabilities exist in many cities, no comprehensive study has been completed to identify attitudinal and contextual barriers to the continuance intention to use the system, particularly in a low-income developing country. Thus, due to limitations faced by qualified experts in this field, the adoption of efficient transportation policy and engineering interventions is still limited. The Technology Continuance Theory (TCT) is employed in this study as a structural equation modeling framework to analyze and quantify relationships based on the data gathered by face-to-face interviews. The mediation/moderation analyses in this paper reveal that (i) stress, (ii) self-efficacy, (ii) social isolation, (iv) mode captivity, (v) experience of usage, and (vi) type of disability are of prime importance in disabled people's intention to reuse the paratransit system. Stress is examined through serial mediation analysis. The analysis reveals that stress due to the system's uncertain attributes significantly influences satisfaction. Self-efficacy and social isolation are presented for the moderation and moderated-mediation analyses. It is found that the urban mobility performance of the system is of more importance to disabled people with lower self-efficacy (high social isolation); however, their stress is not significantly reduced even by increasing the usefulness of the system. The mode captivity is also redefined in this context and entered as a moderator. It is found that, for captive riders, satisfaction does not have a dominating role in the intention to use the paratransit system, as the users are constrained by taking what is available or nothing. The usage experience is another moderator, and our analysis shows that attitude is a determinant of behavior for long-term users, while satisfaction with a transient effect is a dominating factor for short-term ones. Finally, the users are categorized by type of disability , and the moderation analysis shows that for people with physical disabilities, the system's performance is the most influential factor in their satisfaction, while for people with sensory disabilities, other factors related to amenity, safety, and support are also prominent. This paper ultimately presents explicit and thorough policy considerations and practical implications. The article's concluding remark will comprise a disclosure of previously undisclosed insights that underlie the study's findings. • Transportation systems are a basis of activity participation of disabled people. • We try to investigate factors the operator of a paratransit system must consider to attract and retain users. • Technology Continuance Theory is employed in this study as a structural equation modeling framework. • This analysis finally suggests detailed, clear, and specific engineering measures. [ABSTRACT FROM AUTHOR]

The first part of the current study highlights the importance of developing a clean hydrogen supply chain (CHSC) in Iran to enhance energy security and contribute to global efforts towards sustainable energy transition. Depending on whether the focus is on domestic consumption or export of hydrogen, the study provides valuable insights into the prioritization of CHSC development pathways via multi-criteria decision-making tools (AHP, AHOP-TOPSIS, and AHP-VIKOR). The results show that for domestic hydrogen consumption (internal scenario), the best path of the supply chain includes blue hydrogen from steam methane reforming coupled with carbon capture and storage, H 2 -carriers (ammonia and methanol), natural gas grid for transportation and distribution, and industry sector as the end user. In the external scenario, however, the best path of the CHSC consists of green hydrogen production based on solar energy, and the other parts of the path include liquid hydrogen as the storage method, ship as the distribution and transportation, and industrial sector as the end user. By identifying the most suitable methods and technologies for each stage of the supply chain, this paper offers a comprehensive analysis that can guide decision-making in CHSC development. [ABSTRACT FROM AUTHOR]

In continuation of the previous part, this paper comprehensively focuses on energy, exergy, exergo-economic, and environmental (4E) analyses of two prioritized clean hydrogen (H 2) supply chains pathways. The first scenario relies on domestic demand , involves blue H 2 production and storage using steam methane reforming coupled with carbon capture and storage through H 2 conversion to hydrogen carriers in liquid form (ammonia and methanol) aligned with the existing infrastructure. The second scenario involves green H 2 production from photovoltaic panels as the power source follows by storing H 2 in liquid form which is more suitable to export. The findings indicate that the blue H 2 system outperforms the green H 2 system in terms of energy and exergy, boasting 41.38% and 22.15% compared to 12.16% and 12.75%, respectively. However, when examining economic parameters, the blue H 2 system shows a total cost rate of 1976 US$/h, a net profit of $747.2 million (mn) at the end of its lifetime, and a payback period of approximately nine years. Conversely, the green H 2 system demonstrates economic indicators of a 125.2 $/h as a total cost rate, $1.26 billion (bn) net profit, and an 8.17 year payback period. Moreover, the production cost rates for blue and green H 2 are 1.32 $/kg and 2.55 $/kg, respectively; while the CO 2 emissions rate in the blue H 2 system is 10.71 kg/s. The green H 2 system prevents 19.84 kg/s of CO 2 emissions resulted a significant environmental cost reduction of 2.28 $/kg H 2. This analysis facilitates a thorough comparison of these two clean H 2 production and storage systems, offering insights for policymakers across performance, economic, and environmental dimensions. • A comprehensive 4E analysis was applied for the selected H 2 value chain scenarios. • Blue H 2 scenario is superior economically while environmentally the green one takes precedence. • Carbon credit makes the green H 2 scenario emerges frontrunner utilizing PV solar technology. • Methanol was recognized as the most cost-effective H 2 carrier for storage in Iran. [ABSTRACT FROM AUTHOR]

A growing number of cities are turning to green infrastructure as a way to manage climate change, nature recovery and unsustainable development. Although such an approach has been at the centre of thinking about development in many cities in the global North, there have been few interventions in cities of the global South. Only recently have scholars paid attention to how uneven land-use regulations in greening approaches toward informal settlements can exacerbate inequalities and result in green gentrification, residents' displacement and the relocation of informal communities. Eviction policies and the relocation of informal settlements have been justified by planning authorities under the banner of ecological improvement and climate adaptation. However, the eviction and replacement policies are often resisted by the residents of informal settlements. This paper builds upon and extends this nascent bank of knowledge by mobilising the conflicting rationalities framework to illustrate how green interventions conventionally are framed as a 'win-win' can exacerbate inequitable urban redevelopments, leading to exclusion and conflict. The paper qualitatively examines a case of greening plans in an informal settlement in Tehran metropolitan area, Iran. Thus, it also provides knowledge on a context that has received comparatively scant coverage in the academic literature on urban greening. [ABSTRACT FROM AUTHOR]

Rapid growth in population and economic development in Iran and its neighboring countries has resulted in a dramatic increase in electricity demand over the past few years. A substantial amount of electricity must be produced to meet this growing demand in this region. By installing conventional fossil power plants, using renewable energy, and interconnecting with neighboring countries, this growing demand can be accomplished. The main aim of this research is to identify prospects and challenges in the electrical sector around Iran. To this end, this paper evaluates the existing and future state of the electrical sector in Iran and its neighboring countries. Despite the fact that there is still a long way to go in terms of renewable energy in this region, there are some promising targets for the future that will allow fossil fuels to be replaced effectively. Notwithstanding this, we should point out that a number of cross-border interconnection projects are being pursued by Iran and its neighbors in order to integrate their networks and supply this growing demand. Finally, this study presents an overview of the future prospects and challenges of electricity capacity expansion, cross-border interconnections, and electricity transmission corridors in this region. The review results provide comprehensive background knowledge to all the researchers in order to investigate further into the field. This paper assists energy policy makers regarding the development of sustainable energy planning. • •In addition to providing a general overview of the geographical location, population growth, and economic indicators of Iran and its neighboring countries, this paper presents the current state of the Iranian electricity sector and its neighboring countries in light of the trends in electricity production, energy mix, price, and exports and imports. • •This paper explores the existing and future states of electricity capacity expansion in the region, specifically focusing on renewable energy sources. • •The existing and future states of cross-border electricity interconnections are examined to present a comprehensive overview of the cross-border interconnections in this region. • •This region's potential electricity transmission corridors are proposed to create the opportunity to transmit electricity across countries. [ABSTRACT FROM AUTHOR]

As an intermediate region between the Central Iranian Plateau and Central Asia, the northeast of Iran was at the heart of Pleistocene populations' movements between both regions. The Paleolithic survey conducted in the Jovin-Joghatai plain permitted to identify six sites. In this paper, these newfound sites are introduced and the raw material sources used at them are analyzed through petrographic tests before putting their surface assemblages into the context of the Paleolithic eastern Iran. The paper results in the identification of two sites characterized by the so-called pebble-tools. Notwithstanding some slight differences, the assemblages seem to be closed to those from the Paleolithic sites of northeast Iran, such as Abarvan in Kashafrud and Mushan Tepeh in Neyshabur as well as Kuldara in Tajikistan. On the other hand, they display disparities with the lithic industries of western Turkmenistan, Yangadja site as well as Dagh Jazireh in eastern Iran. The remaining four sites are attributable to the Middle Paleolithic on account of their affinities with centers in Central Asia such as Teshik-Tash in Uzbekistan, and Jahan Abad, Damghani, and Kalat-eShur in eastern Iran as well as the Mirak complex in the central plateau. Yet, no significant correlations seem to exist between the Middle Paleolithic industries of the Jovin-Joghatai plain and those of Kiaram and Khunik caves in eastern Iran. [ABSTRACT FROM AUTHOR]

These two-part paper series aim to optimize the rates of inspection-based maintenance or routine-test on power system equipment. Part I introduced a novel probabilistic model using Markov chains for the inspection-based maintenance and derived the relationships between the transition rates. Part II demonstrates how the proposed method can effectively address budget and data availability constraints. It also presents the derived relationships between the optimal preventive maintenance rate and risk indices. The results presented in this paper are based on an industrial project conducted to optimize the preventive maintenance period of high-voltage equipment in Iran. Additionally, this companion paper provides practical insights, including implementation remarks, and a comprehensive dataset for failure and defect analysis, which proves useful for future research and applications. • A case study on a real sub-transmission substation for the proposed model. • Derived relations between the optimal preventive maintenance rate and risk indices. • Analysis of the simultaneity in the inspection of series components. • How to overcome data and budget limitations by the proposed sensitivity analysis. • Practically, how to use the available data to optimize the inspection rate. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of water and emission limitations on expansion planning of thermal power plants (TPPs), in Iran. TPPs need considerable amount of water for their cooling system. In Iran, water crisis of recent years has made it impossible to operate existing steam power plants in some areas, during summer season. This paper considers water availability and cost in generation expansion planning (GEP) of Iran. There are different types of water resources such as rivers, dams, treated wastewater, seawater, seawater transmission line, saline groundwater, and groundwater. Considering available capacity of water resources in each area and sorting their extraction cost in ascending order, some water consumption cost curves are derived. For this purpose, data of about 200 water resources located in different regions of Iran are analyzed. Provided GEP model includes a 15-years planning horizon and analyzes several scenarios of water availability. Although, in low water price scenarios, air temperature and height above sea level have more impact on placement of TPPs, it is different for high water price scenarios. Also, the effect of emission-related policies on GEP decisions is analyzed by considering various fuel types of TPPs, penalties for emissions, and global restrictions on greenhouse gas production. • Proposes an optimization model for generation expansion planning (GEP) that considers environmental constraints. • Analyzes the availability and price of different water resources in Iran and their effects on GEP decisions. • Examines the impact of temperature and height above sea level, on the siting of thermal power plants in Iran. • Considers decarbonization policies in the expansion planning of power plants in Iran. [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]

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. • This paper adopted a resource allocation model in DEA with undesired outputs. • Our resource allocation model is sensitive to uncertainty of the data. • A stochastic resource allocation model is given when we have random data and undesirable products. • An application on 21 electricity production & distribution companies for eight years is given. [ABSTRACT FROM AUTHOR]

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s • The Grell convective scheme applied in RegCM4 has been successful in simulating precipitation extremes in Iran. • The RegCM4 has been able to capture precipitation extremes well in Iran. • Consecutive dry days (CDD) are increasing in most parts of Iran. [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]

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh. [ABSTRACT FROM AUTHOR]

To settle in a given area, man has to deal with the surrounding environment and the geomorphological processes that shaped it. Landscape defines the available resources and landscape changes are related to tectonics, hydrography and variations in climate and biota. To cope with environmental limitations, man developed efficient techniques and capabilities to survive, making the best use of natural resources. The Fars arc (southern Iran) is known for being the cradle of some of the earliest civilizations in Iran. It comprehends the southeastern sector of the Zagros Mountains, a Cenozoic orogen formed after the collision between Arabia and central Iran. The landscape is dominated by a series of folding structures which isolate internal basins. This configuration, together with arid climate conditions, limited biological resources, and the occurrence of natural disasters such as earthquakes, floods, landslides, water salinity, biological/climatic changes and droughts, all dictated the development and pattern of human settlements. This paper aims to give a broad overview on the deep influence exerted by geomorphological processes, such as tectonics, earthquakes, landslides, sea level fluctuations, and climate changes, on the emergence and development of human settlements in the Fars arc which, with its long-lasting and documented civilization history, represents an ideal test site to study the interaction between nature and man. [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 research is aimed at comparing the performance of a proposed data-driven VIKOR method with its knowledge-driven version for porphyry copper prospectivity modeling in the Sardouyeh district, Kerman province, SE Iran. Sixteen exploratory layers from various sources were first nominated for this purpose. These layers were provided from the following sources: (1) multi-element geochemical signatures derived from principal component analysis (PCA); (2) distribution of geophysical footprints derived from aeromagnetic and potassium radioelement surveys; (3) proximity to ore host rocks and tectonic controls; and (4) proximity to hydrothermal alterations. For better comprehension of exploration layers, their values were transferred to the same range [0–1] using fuzzy logic. For predictive modeling of porphyry-type copper mineralization, an improved data-driven VIKOR model was introduced in this paper that its main foundation is based on the calculation of criteria and their relevant classes weights in a data-driven way using prediction-area (P-A) plots and normalized density index (N d), respectively. This methodology was applied twice: once on all exploratory layers and again only on efficient evidence layers. In order to compare and evaluate the performance of the novel methodology introduced for predicting high-favorable areas associated with Cu-porphyry deposits, on the basis of expert opinion and an Analytic hierarchy process (AHP) approach, the AHP-VIKOR procedure was implemented as a knowledge-driven method. A success-rate curve was employed to accurately compare the performance of three models, quantitatively. The findings reveal that the improved data-driven VIKOR approach using efficient evidence layers covered 80 % of Cu occurrences in only 6 % of the total area of the region, which led to much more precise and high-quality outcomes. Moreover, it provides more effective performance to be employed for more detailed exploration programs in this study area and similar studies in other regions. • Discovery of mineral reserves plays a major role in economy and industry. • Knowledge-based and data-based methods are used to explore mineral deposits. • Unlike knowledge-based methods, data-driven methods reduce uncertainty. • Developing a novel data-based VIKOR algorithm is useful in Cu prospectivity mapping. • Using data-based VIKOR is more accurate with involvement of only efficient criteria. [ABSTRACT FROM AUTHOR]

The volcanism of the Arasbaran region, northwest Iran, is characterized by multiple magmatic pulses from Cretaceous to Quaternary related to the consumption of the Neotethys oceanic basin and the subsequent continental collision between the Arabia and the Eurasian plates. In this paper we focus on the Eocene igneous products, which show wide compositional variations, ranging from shoshonite to tephrite and phonolite. They may be further grouped into leucite (analcime)-bearing and leucite-free rock types on the basis of their rock-forming minerals. Leucite-bearing and leucite-free Eocene magmatic rocks are geographically separate outcropping in the WNW and ESE part of the Ahar-Arasbaran region, respectively. K Ar dating show leucite-bearing rocks (39.4–39.6 +/− 1.0 Ma) being slightly younger with respect to leucite-free rocks (41.0–41.9 +/− 1.0 Ma). The two rock types are differentiated by each other in terms of silica saturation degrees but display similar incompatible trace elements distributions, typical of subduction-related volcanic rocks. Indeed, clear depletions in HFSE (e.g., Nb, Ta, Ti, Zr) and enrichments in LILE (e.g., Ba, K) and Pb are shown. The leucite-bearing volcanic rocks are strongly silica-undersaturated (Δq ca. -35) and show higher LILE/HFSE, LILE/REE, Ba/La (30–90) and Ba/Th (up to 520) values with respect to leucite-free rocks (Δ q from 0 to −15; Ba/La up to 30). Leucite-bearing and -free groups also show distinct Sr-Nd-Pb isotopic compositions, with the former having less radiogenic Sr (87Sr/86Sr 0.704424–0.704634) and Pb (206Pb/204Pb 18.58–18.65, 207Pb/204Pb 15.57–15.60, 208Pb/204Pb 38.63–38.71) and more radiogenic Nd (143Nd/144Nd 0.512695–0.512791), with respect to the latter ones (87Sr/86Sr 0.704481–0.705669, 206Pb/204Pb 18.65–18.75, 207Pb/204Pb 15.61–15.64, 208Pb/204Pb 38.65–38.87, 143Nd/144Nd 0.512572–0.512623). Geochemical and isotopic compositions, coupled with the strong silica-undersaturated character, of leucite-bearingrocks suggest in their mantle source the involvement of metasomatizing partial melts from subducted altered oceanic crust and subordinate carbonate-bearing sediments. On the other hand, the compositions of leucite-free igneous rocks are compatible with the involvement of a relatively higher contribution of partial melts from terrigenous (carbonate-poor) subducted sediments. The close spatial association and the relative geographical/stratigraphic position of these products indicate diachronous metasomatic events in the mantle wedge underlying the Arasbaran area that could have been originated by the late arrival of carbonate-rich sediments at depth during slab steepening and incipient roll-back preceding the continental collision. K Ar dating indicates that the Arasbaran magmatism was triggered by a late geodynamic event, during middle Eocene, plausibly consisting of re-adjusting of isotherms that heated the veined mantle wedge following the slab migration after roll-back. The slightly younger age of leucite-bearing rocks with respect to leucite-free rocks, coupled with the lower melting degree of the former may suggest an evolution of the local thermal regime with the progressive involvement of portions of the mantle wedge closer to the subducted plate. • A subduction-related magmatic pulse occurred in NW Iran at ca. 40 Ma. • Shoshonite leucite-free slightly preceded UK leucite-bearing volcanism. • Leucite-free magma source was metasomatised by dominant LC-sediment melts. • Leucite-bearing magmas was metasomatised by dominant HC-sediment melts. • Leucite-bearing rocks share similarities with Neapolitan Roman-type UK rocks. [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]

Remote sensing data can be utilised for regional mapping of the Earth's surface to enhance structural interpretation and mineral prospecting. To this end, satellite multispectral sensors such as the Advanced Space borne Thermal Emission and Reflectance (ASTER) with six channels in the shortwave infrared and five channels in the thermal area is helpful in detecting alteration and mineralisation zones in areas with good rock exposures. This study has investigated and detected hydrothermal alteration zones and mapped structural elements associated with mafic volcanic rocks-related copper mineralisation in the northwest of the Nain district in Central Iran. In this study, we processed ASTER data (14 bands). We generated maps that depict the distribution of alteration minerals (e.g., sericite, kaolinite, chlorite, and calcite) related to copper mineralisation using various techniques such as different band ratio images, False-colour composition (RGB), Matched Filtering (MF), and Spectral Angle Mapper (SAM). Follow-up ground proofing validated the analysis of results from the ASTER data. The study established the regional distribution of hydrothermal alteration zones (i.e., phyllic, argillic, and propylitic). The regional distribution and extent of these alteration zones are associated with regional structures that served as focusing conduits for the buoyant hypogene mineralizing fluids. The results show that ASTER imagery is useful in mapping the extent of the hydrothermal alteration and lithological units and can thus be used to target hydrothermal ore deposits with large alteration footprints. [Display omitted] • In the way of achieving research goals, the paper presents new structural and Mineralisation data which are used for the first time here. The most striking points to emerge from this research are as follows: • Based on structural studies (RS and field study), the regional fault pattern exhibited three major trends: northwest-southeast, northeast-southwest, and east-west. • Mineralisation in the northwestern Nain is associated with hydrothermal activities and passage of the associated fluids through the fractures and Eocene enclosing rocks (basaltic-andesite, basalt, and volcanic breccia). • Copper mineralisation events are directly associated with the main faults and fractures of the area (Kacho Mesqal and Zafarghand faults) and related secondary faults, according to an investigation of faulting patterns concerning alteration zones and mineralisation zones. • Examining areas with a high density of fractures in the distance between the Kacho Mesqal and Zafarghand faults reveals a correlation between copper mineralisation events and the zones with the highest fracture density in the investigated area. [ABSTRACT FROM AUTHOR]

[Display omitted] The Zagros orogenic belt is a major mountain system located in southwestern Iran and extending into northeastern Iraq and southeastern Turkey. It was formed as a result of the Late Paleogene-Early Neogene Arabian-Eurasian continental collision, where its proto-basin was impacted by the tectonic evolution during the Proto-Tethys and Paleo-Tethys development. The entire evolution history can be summarized through an integrated model of three Wilson cycles. This paper aims to provide an understanding of the complexities of plate motions that led to the evolution of the Zagros Fold-and-Thrust Belt, by integrating the time and space evolution of supercontinents. A comprehensive review of the Arabian plate tectonics suggests three main tectonic stages, including a period of rifting from the Late Cambrian to Carboniferous, a period of ocean closure during the Late Triassic and Jurassic, and a final collision during the Cenozoic. These tectonic evolution stages encompassed multiple episodes of basin uplifting and subsidence, marked by wide-spread fault reactivation and fold deformation. Three styles of deformation were identified and characterized, including thin-skinned, thick-skinned, and basement deformation. The closure of the Neo-Tethys Ocean resulted in compressional tectonics that reactivated dormant faults, and emplaced gypsum-salt sedimentary caprock. The genetic analysis concurrently proposes a scientific framework for the characterization of structural deformation in similar convergent plate systems globally. [ABSTRACT FROM AUTHOR]

This paper tries to simulate and discuss the possible solutions that improve the economic and technical performances of a battery-less renewable energy-powered BWRO system according to the real climate conditions. The systems are considered in two capacities (50 and 100 m3/day) and are powered using photovoltaic panels, wind turbines, or both. Four scenarios are considered to couple the RO unit with renewable energy (RE) sources: simple; elevated brackish water tank; variable frequency drive; and a combination of the latter two. First, the simple approach is optimized for the lowest levelized cost of water, and then the other three scenarios are analyzed based on the optimized results of the first scenario. Results indicate that the hybrid system can provide more water (13,500 and 24,900 m3/year). However, due to its homogenous nature and fewer fluctuations, the PV-based system shows better results from several viewpoints, such as specific energy consumption (1.08 kWh/m3 for both capacities) and unused energy ratio (0.32 and 0.28 for 50 and 100 m3/day devices). Also, the suggested scenarios improve the economic performance of the systems. With the modifications of scenario 4, the PV-based system enhances more and shows the lowest value of levelized cost of water (1.09 $/m3). • It is tried to present feasible methods for standalone BWRO units. • Technical and economic analyses are conducted for the systems. • The case study is a water-deprived region in Iran. • A system with an elevated BW tank with VFD-aided pump shows the best result. [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]

[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]

The assessment of heavy metal(oid) concentrations in agricultural soil holds significant importance due to its impact on crop safety and quality, as well as the potential risks it poses to human health. This review utilized secondary online published data to evaluate the contamination levels of seven HMs (As, Cd, Cu, Cr, Ni, Pb, and Zn) in Iranian farmland soils. As a result, 31 research papers were collected to compile a dataset offering a comprehensive analysis of HM concentrations, distribution, sources, ecological risks, and potential health risks. The findings indicated that mean concentrations of Ni and Cd exceeded their corresponding permissible levels in farmland soils, while other HMs remained below this threshold. The mean Igeo (geoaccumulation index) values followed the sequence Ni > As > Cu > Cr > Zn > Pb > Cd, with Igeo values suggesting low to slight enrichment for the studied HMs. Pollution index (PI) results revealed notable contamination of As in various provinces, including Kurdistan, Hamedan, Tehran, Mazandaran, West Azerbaijan, Kermanshah, Hormozgan, Golestan, and the Kamfiruz region of Fars. Elevated contamination of Pb and Cd was observed in Fars (Shiraz region), and Cr and Ni in the Sabzevar area farmland soils. As, Cd, and Cu exhibited higher ecological risks (EI) compared to Zn, Cr, Ni, and Pb. The potential ecological risks (PER) ranged from 9.9 to 244.6, indicating low to moderate risk levels. Non-carcinogenic assessments indicated that ingestion was the primary exposure pathway to HMs in Iranian farmland soils, followed by inhalation and dermal contact. Total hazard index (HI) values for studied HMs did not exceed 1, indicating no significant non-carcinogenic risk for both population age groups. This study highlights the current lack of well-documented scientific reports on the status of HMs in agricultural farmland soils across 12 other provinces in Iran, emphasizing the need for further investigation. Moreover, the bioavailability of HMs in agricultural soils significantly influences HM content in crops, underscoring the need for future research to ascertain HM concentrations in different soil fractions. • Agricultural practices and geology are the main sources of HMs. • Pollution index of HMs falls in the category of serious to low contamination. • Children are more prone to being affected by HMs in Iran farmland soils. • As and Cr showed the most contribution to the no-carcinogenic and carcinogenic risks. [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]

We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019–2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations. • Proposing a method for analyzing car crashes based on spatio-temporal interactions. • Based on MCDM, a data-driven method used for spatio-temporal analysis. • Developing an AHP-TOPSIS hybrid model for car crash spatio-temporal interaction. • Using Kernel Density Estimation to create the continuous-value maps of car crashes. • Providing a car crash hotspot map including both location and time simultaneously. [ABSTRACT FROM AUTHOR]

Soil plays a crucial role in the provision of ecosystem services, particularly in mountain areas that are frequently regarded as delicate and vulnerable systems. The alteration of vegetation cover is known to impact the various fractions of organic matter and other soil properties, subsequently influencing the activities of microbes and enzymes that play a role in nutrient cycling. However, there is not much information available regarding the effect on soil properties in semi-arid mountain landscapes. Here, we studied different soil features under woodland (dominated by Carpinus orientalis Miller.), shrubland (dominated by Berberis integerrima Bunge.) and grassland (dominated by Festuca ovina L., Dactylis glomerata L. and Bromus danthoniae Trin.) in the north of Iran. In the summer, a total of eighteen soil (0–10 cm depth) samples were collected from each vegetation type. In addition, to investigate the dynamics of soil microclimate and biota population, the same number of soil samples were collected in the fall season. Results indicated that woodland had a more fertile soil and a higher biological activity than the other vegetation types. Soil earthworm groups showed higher densities in the fall season, whereas soil biota population and microbial processes were enhanced in the summer season. In addition, the study area presented hot spots of soil fertility and biological activities in woodland compared with shrubland and grassland. As a conclusion, distinct soil properties are influenced by various types of vegetation (particularly woody species in contrast to grass covers). With the aim of increasing soil functioning or rehabilitating degraded areas, natural resource managers are suggested to establish wood covers (trees or shrubs) rather than grasslands, whenever possible. [Display omitted] • Soil fertility and biological activities decreased in the order woodland > shrubland > grassland. • Soil biota showed higher densities in the fall season. • Soil culturable fungi and bacteria population and microbial processes were enhanced in the summer season. • Different types of vegetation had significantly different C and N contents. • It is suggested to use woody species to improve the soil condition of semi-arid areas. [ABSTRACT FROM AUTHOR]